 ## Part 3-Model Implementation

This post is in continuation of previous post. If you have not read it yet I recommend you to visit here

## Step 5: Model Data

Data Science is a multi-disciplinary field between mathematics (i.e. statistics, linear algebra, etc.), computer science (i.e. programming languages, computer systems, etc.) and business management (i.e. communication, subject-matter knowledge, etc.). Most data scientist come from one of the three fields, so they tend to lean towards that discipline. However, data science is like a three-legged stool, with no one leg being more important than the other. So, this step will require advanced knowledge in mathematics. But don’t worry, we only need a high-level overview, which we’ll cover in this Kernel. Also, thanks to computer science, a lot of the heavy lifting is done for you. So, problems that once required graduate degrees in mathematics or statistics, now only take a few lines of code. Last, we’ll need some business acumen to think through the problem. After all, like training a sight-seeing dog, it’s learning from us and not the other way around.

Machine Learning (ML), as the name suggest, is teaching the machine how-to think and not what to think. While this topic and big data has been around for decades, it is becoming more popular than ever because the barrier to entry is lower, for businesses and professionals alike. This is both good and bad. It’s good because these algorithms are now accessible to more people that can solve more problems in the real-world. It’s bad because a lower barrier to entry means, more people will not know the tools they are using and can come to incorrect conclusions. That’s why I focus on teaching you, not just what to do, but why you’re doing it. Previously, I used the analogy of asking someone to hand you a Philip screwdriver, and they hand you a flathead screwdriver or worst a hammer. At best, it shows a complete lack of understanding. At worst, it makes completing the project impossible; or even worst, implements incorrect actionable intelligence. So now that I’ve hammered (no pun intended) my point, I’ll show you what to do and most importantly, WHY you do it.

First, you must understand, that the purpose of machine learning is to solve human problems. Machine learning can be categorized as: supervised learning, unsupervised learning, and reinforced learning. Supervised learning is where you train the model by presenting it a training dataset that includes the correct answer. Unsupervised learning is where you train the model using a training dataset that does not include the correct answer. And reinforced learning is a hybrid of the previous two, where the model is not given the correct answer immediately, but later after a sequence of events to reinforce learning. We are doing supervised machine learning, because we are training our algorithm by presenting it with a set of features and their corresponding target. We then hope to present it a new subset from the same dataset and have similar results in prediction accuracy.

There are many machine learning algorithms, however they can be reduced to four categories: classification, regression, clustering, or dimensionality reduction, depending on your target variable and data modeling goals. We’ll save clustering and dimension reduction for another day, and focus on classification and regression. We can generalize that a continuous target variable requires a regression algorithm and a discrete target variable requires a classification algorithm. One side note, logistic regression, while it has regression in the name, is really a classification algorithm. Since our problem is predicting if a passenger survived or did not survive, this is a discrete target variable. We will use a classification algorithm from the sklearn library to begin our analysis. We will use cross validation and scoring metrics, discussed in later sections, to rank and compare our algorithms’ performance.

Machine Learning Selection:

Now that we identified our solution as a supervised learning classification algorithm. We can narrow our list of choices.

Machine Learning Classification Algorithms:

### Data Science 101: How to Choose a Machine Learning Algorithm (MLA)

IMPORTANT: When it comes to data modeling, the beginner’s question is always, “what is the best machine learning algorithm?” To this the beginner must learn, the No Free Lunch Theorem (NFLT) of Machine Learning. In short, NFLT states, there is no super algorithm, that works best in all situations, for all datasets. So the best approach is to try multiple MLAs, tune them, and compare them for your specific scenario. With that being said, some good research has been done to compare algorithms, such as Caruana & Niculescu-Mizil 2006 watch video lecture here of MLA comparisons, Ogutu et al. 2011 done by the NIH for genomic selection, Fernandez-Delgado et al. 2014 comparing 179 classifiers from 17 families, Thoma 2016 sklearn comparison, and there is also a school of thought that says, more data beats a better algorithm.

So with all this information, where is a beginner to start? I recommend starting with Trees, Bagging, Random Forests, and Boosting. They are basically different implementations of a decision tree, which is the easiest concept to learn and understand. They are also easier to tune, discussed in the next section, than something like SVC. Below, I’ll give an overview of how-to run and compare several MLAs, but the rest of this Kernel will focus on learning data modeling via decision trees and its derivatives.

```#Machine Learning Algorithm (MLA) Selection and Initialization
MLA = [
#Ensemble Methods
ensemble.BaggingClassifier(),
ensemble.ExtraTreesClassifier(),
ensemble.RandomForestClassifier(),

#Gaussian Processes
gaussian_process.GaussianProcessClassifier(),

#GLM
linear_model.LogisticRegressionCV(),
linear_model.PassiveAggressiveClassifier(),
linear_model.RidgeClassifierCV(),
linear_model.SGDClassifier(),
linear_model.Perceptron(),

#Navies Bayes
naive_bayes.BernoulliNB(),
naive_bayes.GaussianNB(),

#Nearest Neighbor
neighbors.KNeighborsClassifier(),

#SVM
svm.SVC(probability=True),
svm.NuSVC(probability=True),
svm.LinearSVC(),

#Trees
tree.DecisionTreeClassifier(),
tree.ExtraTreeClassifier(),

#Discriminant Analysis
discriminant_analysis.LinearDiscriminantAnalysis(),

XGBClassifier()
]

#split dataset in cross-validation with this splitter class: http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.ShuffleSplit.html#sklearn.model_selection.ShuffleSplit
#note: this is an alternative to train_test_split
cv_split = model_selection.ShuffleSplit(n_splits = 10, test_size = .3, train_size = .6, random_state = 0 ) # run model 10x with 60/30 split intentionally leaving out 10%

#create table to compare MLA metrics
MLA_columns = ['MLA Name', 'MLA Parameters','MLA Train Accuracy Mean', 'MLA Test Accuracy Mean', 'MLA Test Accuracy 3*STD' ,'MLA Time']
MLA_compare = pd.DataFrame(columns = MLA_columns)

#create table to compare MLA predictions
MLA_predict = data1[Target]

#index through MLA and save performance to table
row_index = 0
for alg in MLA:

#set name and parameters
MLA_name = alg.__class__.__name__
MLA_compare.loc[row_index, 'MLA Name'] = MLA_name
MLA_compare.loc[row_index, 'MLA Parameters'] = str(alg.get_params())

#score model with cross validation: http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_validate.html#sklearn.model_selection.cross_validate
cv_results = model_selection.cross_validate(alg, data1[data1_x_bin], data1[Target], cv  = cv_split)

MLA_compare.loc[row_index, 'MLA Time'] = cv_results['fit_time'].mean()
MLA_compare.loc[row_index, 'MLA Train Accuracy Mean'] = cv_results['train_score'].mean()
MLA_compare.loc[row_index, 'MLA Test Accuracy Mean'] = cv_results['test_score'].mean()
#if this is a non-bias random sample, then +/-3 standard deviations (std) from the mean, should statistically capture 99.7% of the subsets
MLA_compare.loc[row_index, 'MLA Test Accuracy 3*STD'] = cv_results['test_score'].std()*3   #let's know the worst that can happen!

#save MLA predictions - see section 6 for usage
alg.fit(data1[data1_x_bin], data1[Target])
MLA_predict[MLA_name] = alg.predict(data1[data1_x_bin])

row_index+=1

#print and sort table: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.sort_values.html
MLA_compare.sort_values(by = ['MLA Test Accuracy Mean'], ascending = False, inplace = True)
MLA_compare
#MLA_predict
```
```#barplot using https://seaborn.pydata.org/generated/seaborn.barplot.html
sns.barplot(x='MLA Test Accuracy Mean', y = 'MLA Name', data = MLA_compare, color = 'm')

#prettify using pyplot: https://matplotlib.org/api/pyplot_api.html
plt.title('Machine Learning Algorithm Accuracy Score \n')
plt.xlabel('Accuracy Score (%)')
plt.ylabel('Algorithm')
```

## 5.1 Evaluate Model Performance

Let’s recap, with some basic data cleaning, analysis, and machine learning algorithms (MLA), we are able to predict passenger survival with ~82% accuracy. Not bad for a few lines of code. But the question we always ask is, can we do better and more importantly get an ROI (return on investment) for our time invested? For example, if we’re only going to increase our accuracy by 1/10th of a percent, is it really worth 3-months of development. If you work in research maybe the answer is yes, but if you work in business mostly the answer is no. So, keep that in mind when improving your model.

### Data Science 101: Determine a Baseline Accuracy

Before we decide how-to make our model better, let’s determine if our model is even worth keeping. To do that, we have to go back to the basics of data science 101. We know this is a binary problem, because there are only two possible outcomes; passengers survived or died. So, think of it like a coin flip problem. If you have a fair coin and you guessed heads or tail, then you have a 50-50 chance of guessing correct. So, let’s set 50% as the worst model performance; because anything lower than that, then why do I need you when I can just flip a coin?

Okay, so with no information about the dataset, we can always get 50% with a binary problem. But we have information about the dataset, so we should be able to do better. We know that 1,502/2,224 or 67.5% of people died. Therefore, if we just predict the most frequent occurrence, that 100% of people died, then we would be right 67.5% of the time. So, let’s set 68% as bad model performance, because again, anything lower than that, then why do I need you, when I can just predict using the most frequent occurrence.

### Data Science 101: How-to Create Your Own Model

Our accuracy is increasing, but can we do better? Are there any signals in our data? To illustrate this, we’re going to build our own decision tree model, because it is the easiest to conceptualize and requires simple addition and multiplication calculations. When creating a decision tree, you want to ask questions that segment your target response, placing the survived/1 and dead/0 into homogeneous subgroups. This is part science and part art, so let’s just play the 21-question game to show you how it works. If you want to follow along on your own, download the train dataset and import into Excel. Create a pivot table with survival in the columns, count and % of row count in the values, and the features described below in the rows.

Remember, the name of the game is to create subgroups using a decision tree model to get survived/1 in one bucket and dead/0 in another bucket. Our rule of thumb will be the majority rules. Meaning, if the majority or 50% or more survived, then everybody in our subgroup survived/1, but if 50% or less survived then if everybody in our subgroup died/0. Also, we will stop if the subgroup is less than 10 and/or our model accuracy plateaus or decreases. Got it? Let’s go!

Question 1: Were you on the Titanic? If Yes, then majority (62%) died. Note our sample survival is different than our population of 68%. Nonetheless, if we assumed everybody died, our sample accuracy is 62%.

Question 2: Are you male or female? Male, majority (81%) died. Female, majority (74%) survived. Giving us an accuracy of 79%.

Question 3A (going down the female branch with count = 314): Are you in class 1, 2, or 3? Class 1, majority (97%) survived and Class 2, majority (92%) survived. Since the dead subgroup is less than 10, we will stop going down this branch. Class 3, is even at a 50-50 split. No new information to improve our model is gained.

Question 4A (going down the female class 3 branch with count = 144): Did you embark from port C, Q, or S? We gain a little information. C and Q, the majority still survived, so no change. Also, the dead subgroup is less than 10, so we will stop. S, the majority (63%) died. So, we will change females, class 3, embarked S from assuming they survived, to assuming they died. Our model accuracy increases to 81%.

Question 5A (going down the female class 3 embarked S branch with count = 88): So far, it looks like we made good decisions. Adding another level does not seem to gain much more information. This subgroup 55 died and 33 survived, since majority died we need to find a signal to identify the 33 or a subgroup to change them from dead to survived and improve our model accuracy. We can play with our features. One I found was fare 0-8, majority survived. It’s a small sample size 11-9, but one often used in statistics. We slightly improve our accuracy, but not much to move us past 82%. So, we’ll stop here.

Question 3B (going down the male branch with count = 577): Going back to question 2, we know the majority of males died. So, we are looking for a feature that identifies a subgroup that majority survived. Surprisingly, class or even embarked didn’t matter like it did for females, but title does and gets us to 82%. Guess and checking other features, none seem to push us past 82%. So, we’ll stop here for now.

You did it, with very little information, we get to 82% accuracy. On a worst, bad, good, better, and best scale, we’ll set 82% to good, since it’s a simple model that yields us decent results. But the question still remains, can we do better than our handmade model?

Before we do, let’s code what we just wrote above. Please note, this is a manual process created by “hand.” You won’t have to do this, but it’s important to understand it before you start working with MLA. Think of MLA like a TI-89 calculator on a Calculus Exam. It’s very powerful and helps you with a lot of the grunt work. But if you don’t know what you’re doing on the exam, a calculator, even a TI-89, is not going to help you pass. So, study the next section wisely.

```#IMPORTANT: This is a handmade model for learning purposes only.
#However, it is possible to create your own predictive model without a fancy algorithm :)

#coin flip model with random 1/survived 0/died

#iterate over dataFrame rows as (index, Series) pairs: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.iterrows.html
for index, row in data1.iterrows():
#random number generator: https://docs.python.org/2/library/random.html
if random.random() > .5:     # Random float x, 0.0 <= x < 1.0
data1.set_value(index, 'Random_Predict', 1) #predict survived/1
else:
data1.set_value(index, 'Random_Predict', 0) #predict died/0

#score random guess of survival. Use shortcut 1 = Right Guess and 0 = Wrong Guess
#the mean of the column will then equal the accuracy
data1['Random_Score'] = 0 #assume prediction wrong
data1.loc[(data1['Survived'] == data1['Random_Predict']), 'Random_Score'] = 1 #set to 1 for correct prediction
print('Coin Flip Model Accuracy: {:.2f}%'.format(data1['Random_Score'].mean()*100))

#we can also use scikit's accuracy_score function to save us a few lines of code
#http://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html#sklearn.metrics.accuracy_score
print('Coin Flip Model Accuracy w/SciKit: {:.2f}%'.format(metrics.accuracy_score(data1['Survived'], data1['Random_Predict'])*100))
```
```#group by or pivot table: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.groupby.html
pivot_female = data1[data1.Sex=='female'].groupby(['Sex','Pclass', 'Embarked','FareBin'])['Survived'].mean()
print('Survival Decision Tree w/Female Node: \n',pivot_female)

pivot_male = data1[data1.Sex=='male'].groupby(['Sex','Title'])['Survived'].mean()
print('\n\nSurvival Decision Tree w/Male Node: \n',pivot_male)
```
```Survival Decision Tree w/Female Node:
Sex     Pclass  Embarked  FareBin
female  1       C         (14.454, 31.0]     0.666667
(31.0, 512.329]    1.000000
Q         (31.0, 512.329]    1.000000
S         (14.454, 31.0]     1.000000
(31.0, 512.329]    0.955556
2       C         (7.91, 14.454]     1.000000
(14.454, 31.0]     1.000000
(31.0, 512.329]    1.000000
Q         (7.91, 14.454]     1.000000
S         (7.91, 14.454]     0.875000
(14.454, 31.0]     0.916667
(31.0, 512.329]    1.000000
3       C         (-0.001, 7.91]     1.000000
(7.91, 14.454]     0.428571
(14.454, 31.0]     0.666667
Q         (-0.001, 7.91]     0.750000
(7.91, 14.454]     0.500000
(14.454, 31.0]     0.714286
S         (-0.001, 7.91]     0.533333
(7.91, 14.454]     0.448276
(14.454, 31.0]     0.357143
(31.0, 512.329]    0.125000
Name: Survived, dtype: float64

Survival Decision Tree w/Male Node:
Sex   Title
male  Master    0.575000
Misc      0.250000
Mr        0.156673
Name: Survived, dtype: float64```
```#handmade data model using brain power (and Microsoft Excel Pivot Tables for quick calculations)
def mytree(df):

#initialize table to store predictions
Model = pd.DataFrame(data = {'Predict':[]})
male_title = ['Master'] #survived titles

for index, row in df.iterrows():

#Question 1: Were you on the Titanic; majority died
Model.loc[index, 'Predict'] = 0

#Question 2: Are you female; majority survived
if (df.loc[index, 'Sex'] == 'female'):
Model.loc[index, 'Predict'] = 1

#Question 3A Female - Class and Question 4 Embarked gain minimum information

#Question 5B Female - FareBin; set anything less than .5 in female node decision tree back to 0
if ((df.loc[index, 'Sex'] == 'female') &
(df.loc[index, 'Pclass'] == 3) &
(df.loc[index, 'Embarked'] == 'S')  &
(df.loc[index, 'Fare'] > 8)

):
Model.loc[index, 'Predict'] = 0

#Question 3B Male: Title; set anything greater than .5 to 1 for majority survived
if ((df.loc[index, 'Sex'] == 'male') &
(df.loc[index, 'Title'] in male_title)
):
Model.loc[index, 'Predict'] = 1

return Model

#model data
Tree_Predict = mytree(data1)
print('Decision Tree Model Accuracy/Precision Score: {:.2f}%\n'.format(metrics.accuracy_score(data1['Survived'], Tree_Predict)*100))

#Accuracy Summary Report with http://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html#sklearn.metrics.classification_report
#Where recall score = (true positives)/(true positive + false negative) w/1 being best:http://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html#sklearn.metrics.recall_score
#And F1 score = weighted average of precision and recall w/1 being best: http://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html#sklearn.metrics.f1_score
print(metrics.classification_report(data1['Survived'], Tree_Predict))
```
```#Plot Accuracy Summary
#Credit: http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html
import itertools
def plot_confusion_matrix(cm, classes,
normalize=False,
title='Confusion matrix',
cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print("Normalized confusion matrix")
else:
print('Confusion matrix, without normalization')

print(cm)

plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)

fmt = '.2f' if normalize else 'd'
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape), range(cm.shape)):
plt.text(j, i, format(cm[i, j], fmt),
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")

plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')

# Compute confusion matrix
cnf_matrix = metrics.confusion_matrix(data1['Survived'], Tree_Predict)
np.set_printoptions(precision=2)

# Plot non-normalized confusion matrix
plt.figure()
plot_confusion_matrix(cnf_matrix, classes=class_names,
title='Confusion matrix, without normalization')

# Plot normalized confusion matrix
plt.figure()
plot_confusion_matrix(cnf_matrix, classes=class_names, normalize=True,
title='Normalized confusion matrix')
```

## 5.11 Model Performance with Cross-Validation (CV)

```In step 5.0, we used sklearn cross_validate function to train, test, and score our model performance.

Remember, it's important we use a different subset for train data to build our model and test data to evaluate our model. Otherwise, our model will be overfitted. Meaning it's great at "predicting" data it's already seen, but terrible at predicting data it has not seen; which is not prediction at all. It's like cheating on a school quiz to get 100%, but then when you go to take the exam, you fail because you never truly learned anything. The same is true with machine learning.

CV is basically a shortcut to split and score our model multiple times, so we can get an idea of how well it will perform on unseen data. It’s a little more expensive in computer processing, but it's important so we don't gain false confidence. This is helpful in a Kaggle Competition or any use case where consistency matters and surprises should be avoided.

In addition to CV, we used a customized sklearn train test splitter, to allow a little more randomness in our test scoring. Below is an image of the default CV split.
```

## 5.12 Tune Model with Hyper-Parameters

When we used sklearn Decision Tree (DT) Classifier, we accepted all the function defaults. This leaves opportunity to see how various hyper-parameter settings will change the model accuracy. (Click here to learn more about parameters vs hyper-parameters.)

However, in order to tune a model, we need to actually understand it. That’s why I took the time in the previous sections to show you how predictions work. Now let’s learn a little bit more about our DT algorithm.

Credit: sklearn

Some advantages of decision trees are:

• Simple to understand and to interpret. Trees can be visualized.
• Requires little data preparation. Other techniques often require data normalization, dummy variables need to be created and blank values to be removed. Note however that this module does not support missing values.
• The cost of using the tree (i.e., predicting data) is logarithmic in the number of data points used to train the tree.
• Able to handle both numerical and categorical data. Other techniques are usually specialized in analyzing datasets that have only one type of variable. See algorithms for more information.
• Able to handle multi-output problems.
• Uses a white box model. If a given situation is observable in a model, the explanation for the condition is easily explained by Boolean logic. By contrast, in a black box model (e.g., in an artificial neural network), results may be more difficult to interpret.
• Possible to validate a model using statistical tests. That makes it possible to account for the reliability of the model.
• Performs well even if its assumptions are somewhat violated by the true model from which the data were generated.

The disadvantages of decision trees include:

• Decision-tree learners can create over-complex trees that do not generalize the data well. This is called overfitting. Mechanisms such as pruning (not currently supported), setting the minimum number of samples required at a leaf node or setting the maximum depth of the tree are necessary to avoid this problem.
• Decision trees can be unstable because small variations in the data might result in a completely different tree being generated. This problem is mitigated by using decision trees within an ensemble.
• The problem of learning an optimal decision tree is known to be NP-complete under several aspects of optimality and even for simple concepts. Consequently, practical decision-tree learning algorithms are based on heuristic algorithms such as the greedy algorithm where locally optimal decisions are made at each node. Such algorithms cannot guarantee to return the globally optimal decision tree. This can be mitigated by training multiple trees in an ensemble learner, where the features and samples are randomly sampled with replacement.
• There are concepts that are hard to learn because decision trees do not express them easily, such as XOR, parity or multiplexer problems.
• Decision tree learners create biased trees if some classes dominate. It is therefore recommended to balance the dataset prior to fitting with the decision tree.

Below are available hyper-parameters and defintions:

class sklearn.tree.DecisionTreeClassifier(criterion=’gini’, splitter=’best’, max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, class_weight=None, presort=False)

```#base model
dtree = tree.DecisionTreeClassifier(random_state = 0)
base_results = model_selection.cross_validate(dtree, data1[data1_x_bin], data1[Target], cv  = cv_split)
dtree.fit(data1[data1_x_bin], data1[Target])

print('BEFORE DT Parameters: ', dtree.get_params())
print("BEFORE DT Training w/bin score mean: {:.2f}". format(base_results['train_score'].mean()*100))
print("BEFORE DT Test w/bin score mean: {:.2f}". format(base_results['test_score'].mean()*100))
print("BEFORE DT Test w/bin score 3*std: +/- {:.2f}". format(base_results['test_score'].std()*100*3))
#print("BEFORE DT Test w/bin set score min: {:.2f}". format(base_results['test_score'].min()*100))
print('-'*10)

#tune hyper-parameters: http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html#sklearn.tree.DecisionTreeClassifier
param_grid = {'criterion': ['gini', 'entropy'],  #scoring methodology; two supported formulas for calculating information gain - default is gini
#'splitter': ['best', 'random'], #splitting methodology; two supported strategies - default is best
'max_depth': [2,4,6,8,10,None], #max depth tree can grow; default is none
#'min_samples_split': [2,5,10,.03,.05], #minimum subset size BEFORE new split (fraction is % of total); default is 2
#'min_samples_leaf': [1,5,10,.03,.05], #minimum subset size AFTER new split split (fraction is % of total); default is 1
#'max_features': [None, 'auto'], #max features to consider when performing split; default none or all
'random_state':  #seed or control random number generator: https://www.quora.com/What-is-seed-in-random-number-generation
}

#print(list(model_selection.ParameterGrid(param_grid)))

#choose best model with grid_search: #http://scikit-learn.org/stable/modules/grid_search.html#grid-search
#http://scikit-learn.org/stable/auto_examples/model_selection/plot_grid_search_digits.html
tune_model = model_selection.GridSearchCV(tree.DecisionTreeClassifier(), param_grid=param_grid, scoring = 'roc_auc', cv = cv_split)
tune_model.fit(data1[data1_x_bin], data1[Target])

#print(tune_model.cv_results_.keys())
#print(tune_model.cv_results_['params'])
print('AFTER DT Parameters: ', tune_model.best_params_)
#print(tune_model.cv_results_['mean_train_score'])
print("AFTER DT Training w/bin score mean: {:.2f}". format(tune_model.cv_results_['mean_train_score'][tune_model.best_index_]*100))
#print(tune_model.cv_results_['mean_test_score'])
print("AFTER DT Test w/bin score mean: {:.2f}". format(tune_model.cv_results_['mean_test_score'][tune_model.best_index_]*100))
print("AFTER DT Test w/bin score 3*std: +/- {:.2f}". format(tune_model.cv_results_['std_test_score'][tune_model.best_index_]*100*3))
print('-'*10)

#duplicates gridsearchcv
#tune_results = model_selection.cross_validate(tune_model, data1[data1_x_bin], data1[Target], cv  = cv_split)

#print('AFTER DT Parameters: ', tune_model.best_params_)
#print("AFTER DT Training w/bin set score mean: {:.2f}". format(tune_results['train_score'].mean()*100))
#print("AFTER DT Test w/bin set score mean: {:.2f}". format(tune_results['test_score'].mean()*100))
#print("AFTER DT Test w/bin set score min: {:.2f}". format(tune_results['test_score'].min()*100))
#print('-'*10)
```

## 5.13 Tune Model with Feature Selection

As stated in the beginning, more predictor variables do not make a better model, but the right predictors do. So another step in data modeling is feature selection. Sklearn has several options, we will use recursive feature elimination (RFE) with cross validation (CV).

```#base model
print('BEFORE DT RFE Training Shape Old: ', data1[data1_x_bin].shape)
print('BEFORE DT RFE Training Columns Old: ', data1[data1_x_bin].columns.values)

print("BEFORE DT RFE Training w/bin score mean: {:.2f}". format(base_results['train_score'].mean()*100))
print("BEFORE DT RFE Test w/bin score mean: {:.2f}". format(base_results['test_score'].mean()*100))
print("BEFORE DT RFE Test w/bin score 3*std: +/- {:.2f}". format(base_results['test_score'].std()*100*3))
print('-'*10)

#feature selection
dtree_rfe = feature_selection.RFECV(dtree, step = 1, scoring = 'accuracy', cv = cv_split)
dtree_rfe.fit(data1[data1_x_bin], data1[Target])

#transform x&y to reduced features and fit new model
#alternative: can use pipeline to reduce fit and transform steps: http://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html
X_rfe = data1[data1_x_bin].columns.values[dtree_rfe.get_support()]
rfe_results = model_selection.cross_validate(dtree, data1[X_rfe], data1[Target], cv  = cv_split)

#print(dtree_rfe.grid_scores_)
print('AFTER DT RFE Training Shape New: ', data1[X_rfe].shape)
print('AFTER DT RFE Training Columns New: ', X_rfe)

print("AFTER DT RFE Training w/bin score mean: {:.2f}". format(rfe_results['train_score'].mean()*100))
print("AFTER DT RFE Test w/bin score mean: {:.2f}". format(rfe_results['test_score'].mean()*100))
print("AFTER DT RFE Test w/bin score 3*std: +/- {:.2f}". format(rfe_results['test_score'].std()*100*3))
print('-'*10)

#tune rfe model
rfe_tune_model = model_selection.GridSearchCV(tree.DecisionTreeClassifier(), param_grid=param_grid, scoring = 'roc_auc', cv = cv_split)
rfe_tune_model.fit(data1[X_rfe], data1[Target])

#print(rfe_tune_model.cv_results_.keys())
#print(rfe_tune_model.cv_results_['params'])
print('AFTER DT RFE Tuned Parameters: ', rfe_tune_model.best_params_)
#print(rfe_tune_model.cv_results_['mean_train_score'])
print("AFTER DT RFE Tuned Training w/bin score mean: {:.2f}". format(rfe_tune_model.cv_results_['mean_train_score'][tune_model.best_index_]*100))
#print(rfe_tune_model.cv_results_['mean_test_score'])
print("AFTER DT RFE Tuned Test w/bin score mean: {:.2f}". format(rfe_tune_model.cv_results_['mean_test_score'][tune_model.best_index_]*100))
print("AFTER DT RFE Tuned Test w/bin score 3*std: +/- {:.2f}". format(rfe_tune_model.cv_results_['std_test_score'][tune_model.best_index_]*100*3))
print('-'*10)
```
```#Graph MLA version of Decision Tree: http://scikit-learn.org/stable/modules/generated/sklearn.tree.export_graphviz.html
import graphviz
dot_data = tree.export_graphviz(dtree, out_file=None,
feature_names = data1_x_bin, class_names = True,
filled = True, rounded = True)
graph = graphviz.Source(dot_data)
graph
```

## Step 6: Validate and Implement

The next step is to prepare for submission using the validation data.

```#compare algorithm predictions with each other, where 1 = exactly similar and 0 = exactly opposite
#there are some 1's, but enough blues and light reds to create a "super algorithm" by combining them
correlation_heatmap(MLA_predict)
```
```#why choose one model, when you can pick them all with voting classifier
#http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.VotingClassifier.html
#removed models w/o attribute 'predict_proba' required for vote classifier and models with a 1.0 correlation to another model
vote_est = [
#Ensemble Methods: http://scikit-learn.org/stable/modules/ensemble.html
('bc', ensemble.BaggingClassifier()),
('etc',ensemble.ExtraTreesClassifier()),
('rfc', ensemble.RandomForestClassifier()),

#Gaussian Processes: http://scikit-learn.org/stable/modules/gaussian_process.html#gaussian-process-classification-gpc
('gpc', gaussian_process.GaussianProcessClassifier()),

#GLM: http://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
('lr', linear_model.LogisticRegressionCV()),

#Navies Bayes: http://scikit-learn.org/stable/modules/naive_bayes.html
('bnb', naive_bayes.BernoulliNB()),
('gnb', naive_bayes.GaussianNB()),

#Nearest Neighbor: http://scikit-learn.org/stable/modules/neighbors.html
('knn', neighbors.KNeighborsClassifier()),

#SVM: http://scikit-learn.org/stable/modules/svm.html
('svc', svm.SVC(probability=True)),

('xgb', XGBClassifier())

]

#Hard Vote or majority rules
vote_hard = ensemble.VotingClassifier(estimators = vote_est , voting = 'hard')
vote_hard_cv = model_selection.cross_validate(vote_hard, data1[data1_x_bin], data1[Target], cv  = cv_split)
vote_hard.fit(data1[data1_x_bin], data1[Target])

print("Hard Voting Training w/bin score mean: {:.2f}". format(vote_hard_cv['train_score'].mean()*100))
print("Hard Voting Test w/bin score mean: {:.2f}". format(vote_hard_cv['test_score'].mean()*100))
print("Hard Voting Test w/bin score 3*std: +/- {:.2f}". format(vote_hard_cv['test_score'].std()*100*3))
print('-'*10)

#Soft Vote or weighted probabilities
vote_soft = ensemble.VotingClassifier(estimators = vote_est , voting = 'soft')
vote_soft_cv = model_selection.cross_validate(vote_soft, data1[data1_x_bin], data1[Target], cv  = cv_split)
vote_soft.fit(data1[data1_x_bin], data1[Target])

print("Soft Voting Training w/bin score mean: {:.2f}". format(vote_soft_cv['train_score'].mean()*100))
print("Soft Voting Test w/bin score mean: {:.2f}". format(vote_soft_cv['test_score'].mean()*100))
print("Soft Voting Test w/bin score 3*std: +/- {:.2f}". format(vote_soft_cv['test_score'].std()*100*3))
print('-'*10)

```
```#IMPORTANT: THIS SECTION IS UNDER CONSTRUCTION!!!! 12.24.17
#UPDATE: This section was scrapped for the next section; as it's more computational friendly.

#WARNING: Running is very computational intensive and time expensive
#code is written for experimental/developmental purposes and not production ready

#tune each estimator before creating a super model
#http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html
grid_n_estimator = [50,100,300]
grid_ratio = [.1,.25,.5,.75,1.0]
grid_learn = [.01,.03,.05,.1,.25]
grid_max_depth = [2,4,6,None]
grid_min_samples = [5,10,.03,.05,.10]
grid_criterion = ['gini', 'entropy']
grid_bool = [True, False]
grid_seed = 

vote_param = [{

#http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.BaggingClassifier.html#sklearn.ensemble.BaggingClassifier
'bc__n_estimators': grid_n_estimator,
'bc__max_samples': grid_ratio,
'bc__oob_score': grid_bool,
'bc__random_state': grid_seed,

#http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html#sklearn.ensemble.ExtraTreesClassifier
'etc__n_estimators': grid_n_estimator,
'etc__criterion': grid_criterion,
'etc__max_depth': grid_max_depth,
'etc__random_state': grid_seed,

'gbc__loss': ['deviance', 'exponential'],
'gbc__learning_rate': grid_ratio,
'gbc__n_estimators': grid_n_estimator,
'gbc__criterion': ['friedman_mse', 'mse', 'mae'],
'gbc__max_depth': grid_max_depth,
'gbc__min_samples_split': grid_min_samples,
'gbc__min_samples_leaf': grid_min_samples,
'gbc__random_state': grid_seed,

#http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier
'rfc__n_estimators': grid_n_estimator,
'rfc__criterion': grid_criterion,
'rfc__max_depth': grid_max_depth,
'rfc__min_samples_split': grid_min_samples,
'rfc__min_samples_leaf': grid_min_samples,
'rfc__bootstrap': grid_bool,
'rfc__oob_score': grid_bool,
'rfc__random_state': grid_seed,

#http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegressionCV.html#sklearn.linear_model.LogisticRegressionCV
'lr__fit_intercept': grid_bool,
'lr__penalty': ['l1','l2'],
'lr__solver': ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'],
'lr__random_state': grid_seed,

#http://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.BernoulliNB.html#sklearn.naive_bayes.BernoulliNB
'bnb__alpha': grid_ratio,
'bnb__prior': grid_bool,
'bnb__random_state': grid_seed,

#http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#sklearn.neighbors.KNeighborsClassifier
'knn__n_neighbors': [1,2,3,4,5,6,7],
'knn__weights': ['uniform', 'distance'],
'knn__algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'],
'knn__random_state': grid_seed,

#http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC
#http://blog.hackerearth.com/simple-tutorial-svm-parameter-tuning-python-r
'svc__kernel': ['linear', 'poly', 'rbf', 'sigmoid'],
'svc__C': grid_max_depth,
'svc__gamma': grid_ratio,
'svc__decision_function_shape': ['ovo', 'ovr'],
'svc__probability': [True],
'svc__random_state': grid_seed,

'xgb__learning_rate': grid_ratio,
'xgb__max_depth': [2,4,6,8,10],
'xgb__tree_method': ['exact', 'approx', 'hist'],
'xgb__objective': ['reg:linear', 'reg:logistic', 'binary:logistic'],
'xgb__seed': grid_seed

}]

#Soft Vote with tuned models
#grid_soft = model_selection.GridSearchCV(estimator = vote_soft, param_grid = vote_param, cv = 2, scoring = 'roc_auc')
#grid_soft.fit(data1[data1_x_bin], data1[Target])

#print(grid_soft.cv_results_.keys())
#print(grid_soft.cv_results_['params'])
#print('Soft Vote Tuned Parameters: ', grid_soft.best_params_)
#print(grid_soft.cv_results_['mean_train_score'])
#print("Soft Vote Tuned Training w/bin set score mean: {:.2f}". format(grid_soft.cv_results_['mean_train_score'][tune_model.best_index_]*100))
#print(grid_soft.cv_results_['mean_test_score'])
#print("Soft Vote Tuned Test w/bin set score mean: {:.2f}". format(grid_soft.cv_results_['mean_test_score'][tune_model.best_index_]*100))
#print("Soft Vote Tuned Test w/bin score 3*std: +/- {:.2f}". format(grid_soft.cv_results_['std_test_score'][tune_model.best_index_]*100*3))
#print('-'*10)

#credit: https://rasbt.github.io/mlxtend/user_guide/classifier/EnsembleVoteClassifier/
#cv_keys = ('mean_test_score', 'std_test_score', 'params')
#for r, _ in enumerate(grid_soft.cv_results_['mean_test_score']):
#    print("%0.3f +/- %0.2f %r"
#          % (grid_soft.cv_results_[cv_keys][r],
#             grid_soft.cv_results_[cv_keys][r] / 2.0,
#             grid_soft.cv_results_[cv_keys][r]))

#print('-'*10)
#WARNING: Running is very computational intensive and time expensive.
#Code is written for experimental/developmental purposes and not production ready!

#Hyperparameter Tune with GridSearchCV: http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html
grid_n_estimator = [10, 50, 100, 300]
grid_ratio = [.1, .25, .5, .75, 1.0]
grid_learn = [.01, .03, .05, .1, .25]
grid_max_depth = [2, 4, 6, 8, 10, None]
grid_min_samples = [5, 10, .03, .05, .10]
grid_criterion = ['gini', 'entropy']
grid_bool = [True, False]
grid_seed = 

grid_param = [
[{
'n_estimators': grid_n_estimator, #default=50
'learning_rate': grid_learn, #default=1
#'algorithm': ['SAMME', 'SAMME.R'], #default=’SAMME.R
'random_state': grid_seed
}],

[{
#BaggingClassifier - http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.BaggingClassifier.html#sklearn.ensemble.BaggingClassifier
'n_estimators': grid_n_estimator, #default=10
'max_samples': grid_ratio, #default=1.0
'random_state': grid_seed
}],

[{
#ExtraTreesClassifier - http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html#sklearn.ensemble.ExtraTreesClassifier
'n_estimators': grid_n_estimator, #default=10
'criterion': grid_criterion, #default=”gini”
'max_depth': grid_max_depth, #default=None
'random_state': grid_seed
}],

[{
#'loss': ['deviance', 'exponential'], #default=’deviance’
'learning_rate': [.05], #default=0.1 -- 12/31/17 set to reduce runtime -- The best parameter for GradientBoostingClassifier is {'learning_rate': 0.05, 'max_depth': 2, 'n_estimators': 300, 'random_state': 0} with a runtime of 264.45 seconds.
'n_estimators': , #default=100 -- 12/31/17 set to reduce runtime -- The best parameter for GradientBoostingClassifier is {'learning_rate': 0.05, 'max_depth': 2, 'n_estimators': 300, 'random_state': 0} with a runtime of 264.45 seconds.
#'criterion': ['friedman_mse', 'mse', 'mae'], #default=”friedman_mse”
'max_depth': grid_max_depth, #default=3
'random_state': grid_seed
}],

[{
#RandomForestClassifier - http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier
'n_estimators': grid_n_estimator, #default=10
'criterion': grid_criterion, #default=”gini”
'max_depth': grid_max_depth, #default=None
'oob_score': [True], #default=False -- 12/31/17 set to reduce runtime -- The best parameter for RandomForestClassifier is {'criterion': 'entropy', 'max_depth': 6, 'n_estimators': 100, 'oob_score': True, 'random_state': 0} with a runtime of 146.35 seconds.
'random_state': grid_seed
}],

[{
#GaussianProcessClassifier
'max_iter_predict': grid_n_estimator, #default: 100
'random_state': grid_seed
}],

[{
#LogisticRegressionCV - http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegressionCV.html#sklearn.linear_model.LogisticRegressionCV
'fit_intercept': grid_bool, #default: True
#'penalty': ['l1','l2'],
'solver': ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'], #default: lbfgs
'random_state': grid_seed
}],

[{
#BernoulliNB - http://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.BernoulliNB.html#sklearn.naive_bayes.BernoulliNB
'alpha': grid_ratio, #default: 1.0
}],

#GaussianNB -
[{}],

[{
#KNeighborsClassifier - http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#sklearn.neighbors.KNeighborsClassifier
'n_neighbors': [1,2,3,4,5,6,7], #default: 5
'weights': ['uniform', 'distance'], #default = ‘uniform’
'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute']
}],

[{
#SVC - http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC
#http://blog.hackerearth.com/simple-tutorial-svm-parameter-tuning-python-r
#'kernel': ['linear', 'poly', 'rbf', 'sigmoid'],
'C': [1,2,3,4,5], #default=1.0
'gamma': grid_ratio, #edfault: auto
'decision_function_shape': ['ovo', 'ovr'], #default:ovr
'probability': [True],
'random_state': grid_seed
}],

[{
'learning_rate': grid_learn, #default: .3
'max_depth': [1,2,4,6,8,10], #default 2
'n_estimators': grid_n_estimator,
'seed': grid_seed
}]
]

start_total = time.perf_counter() #https://docs.python.org/3/library/time.html#time.perf_counter
for clf, param in zip (vote_est, grid_param): #https://docs.python.org/3/library/functions.html#zip

#print(clf) #vote_est is a list of tuples, index 0 is the name and index 1 is the algorithm
#print(param)

start = time.perf_counter()
best_search = model_selection.GridSearchCV(estimator = clf, param_grid = param, cv = cv_split, scoring = 'roc_auc')
best_search.fit(data1[data1_x_bin], data1[Target])
run = time.perf_counter() - start

best_param = best_search.best_params_
print('The best parameter for {} is {} with a runtime of {:.2f} seconds.'.format(clf.__class__.__name__, best_param, run))
clf.set_params(**best_param)

run_total = time.perf_counter() - start_total
print('Total optimization time was {:.2f} minutes.'.format(run_total/60))

print('-'*10)
```
```The best parameter for AdaBoostClassifier is {'learning_rate': 0.1, 'n_estimators': 300, 'random_state': 0} with a runtime of 37.28 seconds.
The best parameter for BaggingClassifier is {'max_samples': 0.25, 'n_estimators': 300, 'random_state': 0} with a runtime of 33.04 seconds.
The best parameter for ExtraTreesClassifier is {'criterion': 'entropy', 'max_depth': 6, 'n_estimators': 100, 'random_state': 0} with a runtime of 68.93 seconds.
The best parameter for GradientBoostingClassifier is {'learning_rate': 0.05, 'max_depth': 2, 'n_estimators': 300, 'random_state': 0} with a runtime of 38.77 seconds.
The best parameter for RandomForestClassifier is {'criterion': 'entropy', 'max_depth': 6, 'n_estimators': 100, 'oob_score': True, 'random_state': 0} with a runtime of 84.14 seconds.
The best parameter for GaussianProcessClassifier is {'max_iter_predict': 10, 'random_state': 0} with a runtime of 6.19 seconds.
The best parameter for LogisticRegressionCV is {'fit_intercept': True, 'random_state': 0, 'solver': 'liblinear'} with a runtime of 9.40 seconds.
The best parameter for BernoulliNB is {'alpha': 0.1} with a runtime of 0.24 seconds.
The best parameter for GaussianNB is {} with a runtime of 0.05 seconds.
The best parameter for KNeighborsClassifier is {'algorithm': 'brute', 'n_neighbors': 7, 'weights': 'uniform'} with a runtime of 5.56 seconds.
The best parameter for SVC is {'C': 2, 'decision_function_shape': 'ovo', 'gamma': 0.1, 'probability': True, 'random_state': 0} with a runtime of 30.49 seconds.
The best parameter for XGBClassifier is {'learning_rate': 0.01, 'max_depth': 4, 'n_estimators': 300, 'seed': 0} with a runtime of 43.57 seconds.
Total optimization time was 5.96 minutes.
----------
```
```#Hard Vote or majority rules w/Tuned Hyperparameters
grid_hard = ensemble.VotingClassifier(estimators = vote_est , voting = 'hard')
grid_hard_cv = model_selection.cross_validate(grid_hard, data1[data1_x_bin], data1[Target], cv  = cv_split)
grid_hard.fit(data1[data1_x_bin], data1[Target])

print("Hard Voting w/Tuned Hyperparameters Training w/bin score mean: {:.2f}". format(grid_hard_cv['train_score'].mean()*100))
print("Hard Voting w/Tuned Hyperparameters Test w/bin score mean: {:.2f}". format(grid_hard_cv['test_score'].mean()*100))
print("Hard Voting w/Tuned Hyperparameters Test w/bin score 3*std: +/- {:.2f}". format(grid_hard_cv['test_score'].std()*100*3))
print('-'*10)

#Soft Vote or weighted probabilities w/Tuned Hyperparameters
grid_soft = ensemble.VotingClassifier(estimators = vote_est , voting = 'soft')
grid_soft_cv = model_selection.cross_validate(grid_soft, data1[data1_x_bin], data1[Target], cv  = cv_split)
grid_soft.fit(data1[data1_x_bin], data1[Target])

print("Soft Voting w/Tuned Hyperparameters Training w/bin score mean: {:.2f}". format(grid_soft_cv['train_score'].mean()*100))
print("Soft Voting w/Tuned Hyperparameters Test w/bin score mean: {:.2f}". format(grid_soft_cv['test_score'].mean()*100))
print("Soft Voting w/Tuned Hyperparameters Test w/bin score 3*std: +/- {:.2f}". format(grid_soft_cv['test_score'].std()*100*3))
print('-'*10)

#12/31/17 tuned with data1_x_bin
#The best parameter for AdaBoostClassifier is {'learning_rate': 0.1, 'n_estimators': 300, 'random_state': 0} with a runtime of 33.39 seconds.
#The best parameter for BaggingClassifier is {'max_samples': 0.25, 'n_estimators': 300, 'random_state': 0} with a runtime of 30.28 seconds.
#The best parameter for ExtraTreesClassifier is {'criterion': 'entropy', 'max_depth': 6, 'n_estimators': 100, 'random_state': 0} with a runtime of 64.76 seconds.
#The best parameter for GradientBoostingClassifier is {'learning_rate': 0.05, 'max_depth': 2, 'n_estimators': 300, 'random_state': 0} with a runtime of 34.35 seconds.
#The best parameter for RandomForestClassifier is {'criterion': 'entropy', 'max_depth': 6, 'n_estimators': 100, 'oob_score': True, 'random_state': 0} with a runtime of 76.32 seconds.
#The best parameter for GaussianProcessClassifier is {'max_iter_predict': 10, 'random_state': 0} with a runtime of 6.01 seconds.
#The best parameter for LogisticRegressionCV is {'fit_intercept': True, 'random_state': 0, 'solver': 'liblinear'} with a runtime of 8.04 seconds.
#The best parameter for BernoulliNB is {'alpha': 0.1} with a runtime of 0.19 seconds.
#The best parameter for GaussianNB is {} with a runtime of 0.04 seconds.
#The best parameter for KNeighborsClassifier is {'algorithm': 'brute', 'n_neighbors': 7, 'weights': 'uniform'} with a runtime of 4.84 seconds.
#The best parameter for SVC is {'C': 2, 'decision_function_shape': 'ovo', 'gamma': 0.1, 'probability': True, 'random_state': 0} with a runtime of 29.39 seconds.
#The best parameter for XGBClassifier is {'learning_rate': 0.01, 'max_depth': 4, 'n_estimators': 300, 'seed': 0} with a runtime of 46.23 seconds.
#Total optimization time was 5.56 minutes.
```
```#prepare data for modeling
print(data_val.info())
print("-"*10)
#data_val.sample(10)

#handmade decision tree - submission score = 0.77990
data_val['Survived'] = mytree(data_val).astype(int)

#decision tree w/full dataset modeling submission score: defaults= 0.76555, tuned= 0.77990
#submit_dt = tree.DecisionTreeClassifier()
#submit_dt = model_selection.GridSearchCV(tree.DecisionTreeClassifier(), param_grid=param_grid, scoring = 'roc_auc', cv = cv_split)
#submit_dt.fit(data1[data1_x_bin], data1[Target])
#print('Best Parameters: ', submit_dt.best_params_) #Best Parameters:  {'criterion': 'gini', 'max_depth': 4, 'random_state': 0}
#data_val['Survived'] = submit_dt.predict(data_val[data1_x_bin])

#bagging w/full dataset modeling submission score: defaults= 0.75119, tuned= 0.77990
#submit_bc = ensemble.BaggingClassifier()
#submit_bc = model_selection.GridSearchCV(ensemble.BaggingClassifier(), param_grid= {'n_estimators':grid_n_estimator, 'max_samples': grid_ratio, 'oob_score': grid_bool, 'random_state': grid_seed}, scoring = 'roc_auc', cv = cv_split)
#submit_bc.fit(data1[data1_x_bin], data1[Target])
#print('Best Parameters: ', submit_bc.best_params_) #Best Parameters:  {'max_samples': 0.25, 'n_estimators': 500, 'oob_score': True, 'random_state': 0}
#data_val['Survived'] = submit_bc.predict(data_val[data1_x_bin])

#extra tree w/full dataset modeling submission score: defaults= 0.76555, tuned= 0.77990
#submit_etc = ensemble.ExtraTreesClassifier()
#submit_etc = model_selection.GridSearchCV(ensemble.ExtraTreesClassifier(), param_grid={'n_estimators': grid_n_estimator, 'criterion': grid_criterion, 'max_depth': grid_max_depth, 'random_state': grid_seed}, scoring = 'roc_auc', cv = cv_split)
#submit_etc.fit(data1[data1_x_bin], data1[Target])
#print('Best Parameters: ', submit_etc.best_params_) #Best Parameters:  {'criterion': 'entropy', 'max_depth': 6, 'n_estimators': 100, 'random_state': 0}
#data_val['Survived'] = submit_etc.predict(data_val[data1_x_bin])

#random foreset w/full dataset modeling submission score: defaults= 0.71291, tuned= 0.73205
#submit_rfc = ensemble.RandomForestClassifier()
#submit_rfc = model_selection.GridSearchCV(ensemble.RandomForestClassifier(), param_grid={'n_estimators': grid_n_estimator, 'criterion': grid_criterion, 'max_depth': grid_max_depth, 'random_state': grid_seed}, scoring = 'roc_auc', cv = cv_split)
#submit_rfc.fit(data1[data1_x_bin], data1[Target])
#print('Best Parameters: ', submit_rfc.best_params_) #Best Parameters:  {'criterion': 'entropy', 'max_depth': 6, 'n_estimators': 100, 'random_state': 0}
#data_val['Survived'] = submit_rfc.predict(data_val[data1_x_bin])

#ada boosting w/full dataset modeling submission score: defaults= 0.74162, tuned= 0.75119
#submit_abc = model_selection.GridSearchCV(ensemble.AdaBoostClassifier(), param_grid={'n_estimators': grid_n_estimator, 'learning_rate': grid_ratio, 'algorithm': ['SAMME', 'SAMME.R'], 'random_state': grid_seed}, scoring = 'roc_auc', cv = cv_split)
#submit_abc.fit(data1[data1_x_bin], data1[Target])
#print('Best Parameters: ', submit_abc.best_params_) #Best Parameters:  {'algorithm': 'SAMME.R', 'learning_rate': 0.1, 'n_estimators': 300, 'random_state': 0}
#data_val['Survived'] = submit_abc.predict(data_val[data1_x_bin])

#gradient boosting w/full dataset modeling submission score: defaults= 0.75119, tuned= 0.77033
#submit_gbc = model_selection.GridSearchCV(ensemble.GradientBoostingClassifier(), param_grid={'learning_rate': grid_ratio, 'n_estimators': grid_n_estimator, 'max_depth': grid_max_depth, 'random_state':grid_seed}, scoring = 'roc_auc', cv = cv_split)
#submit_gbc.fit(data1[data1_x_bin], data1[Target])
#print('Best Parameters: ', submit_gbc.best_params_) #Best Parameters:  {'learning_rate': 0.25, 'max_depth': 2, 'n_estimators': 50, 'random_state': 0}
#data_val['Survived'] = submit_gbc.predict(data_val[data1_x_bin])

#extreme boosting w/full dataset modeling submission score: defaults= 0.73684, tuned= 0.77990
#submit_xgb = XGBClassifier()
#submit_xgb = model_selection.GridSearchCV(XGBClassifier(), param_grid= {'learning_rate': grid_learn, 'max_depth': [0,2,4,6,8,10], 'n_estimators': grid_n_estimator, 'seed': grid_seed}, scoring = 'roc_auc', cv = cv_split)
#submit_xgb.fit(data1[data1_x_bin], data1[Target])
#print('Best Parameters: ', submit_xgb.best_params_) #Best Parameters:  {'learning_rate': 0.01, 'max_depth': 4, 'n_estimators': 300, 'seed': 0}
#data_val['Survived'] = submit_xgb.predict(data_val[data1_x_bin])

#hard voting classifier w/full dataset modeling submission score: defaults= 0.75598, tuned = 0.77990
#data_val['Survived'] = vote_hard.predict(data_val[data1_x_bin])
data_val['Survived'] = grid_hard.predict(data_val[data1_x_bin])

#soft voting classifier w/full dataset modeling submission score: defaults= 0.73684, tuned = 0.74162
#data_val['Survived'] = vote_soft.predict(data_val[data1_x_bin])
#data_val['Survived'] = grid_soft.predict(data_val[data1_x_bin])

#submit file
submit = data_val[['PassengerId','Survived']]
submit.to_csv("../working/submit.csv", index=False)

print('Validation Data Distribution: \n', data_val['Survived'].value_counts(normalize = True))
submit.sample(10)
```
```<class 'pandas.core.frame.DataFrame'>
RangeIndex: 418 entries, 0 to 417
Data columns (total 21 columns):
PassengerId      418 non-null int64
Pclass           418 non-null int64
Name             418 non-null object
Sex              418 non-null object
Age              418 non-null float64
SibSp            418 non-null int64
Parch            418 non-null int64
Ticket           418 non-null object
Fare             418 non-null float64
Cabin            91 non-null object
Embarked         418 non-null object
FamilySize       418 non-null int64
IsAlone          418 non-null int64
Title            418 non-null object
FareBin          418 non-null category
AgeBin           418 non-null category
Sex_Code         418 non-null int64
Embarked_Code    418 non-null int64
Title_Code       418 non-null int64
AgeBin_Code      418 non-null int64
FareBin_Code     418 non-null int64
dtypes: category(2), float64(2), int64(11), object(6)
memory usage: 63.1+ KB
None
----------
Validation Data Distribution:
0    0.633971
1    0.366029
Name: Survived, dtype: float64```

## Step 7: Optimize and Strategize

### Conclusion

Iteration one of the Data Science Framework, seems to converge on 0.77990 submission accuracy. Using the same dataset and different implementation of a decision tree (adaboost, random forest, gradient boost, xgboost, etc.) with tuning does not exceed the 0.77990 submission accuracy. Interesting for this dataset, the simple decision tree algorithm had the best default submission score and with tuning achieved the same best accuracy score.

While no general conclusions can be made from testing a handful of algorithms on a single dataset, there are several observations on the mentioned dataset.

1. The train dataset has a different distribution than the test/validation dataset and population. This created wide margins between the cross validation (CV) accuracy score and Kaggle submission accuracy score.
2. Given the same dataset, decision tree based algorithms, seemed to converge on the same accuracy score after proper tuning.
3. Despite tuning, no machine learning algorithm, exceeded the homemade algorithm. The author will theorize, that for small datasets, a manmade algorithm is the bar to beat.

With that in mind, for iteration two, I would spend more time on preprocessing and feature engineering. In order to better align the CV score and Kaggle score and improve the overall accuracy.