Geeky Codes | AI Engineering, Data Engineering & Machine Learning Tutorials
Unstructured data files consist of a series of bits. The file doesn’t separate the bits from each other in any way. You can’t simply look into the file and see any structure because there isn’t any to see. Unstructured file formats rely on the file user to know how to interpret the data. For example, … Read more
As we have discussed, a Random Forest is an ensemble of Decision Trees, generally trained via the bagging method (or sometimes pasting), typically with max_samples set to the size of the training set. Instead of building a BaggingClassifier and passing it a DecisionTreeClassifier, you can instead use the RandomForestClassifier class, which is more convenient and … Read more
Introduction Storing data in local computer memory represents the fastest and most reliable means to access it. The data could reside anywhere. However, you don’t actually interact with the data in its storage location. You load the data into memory from the storage location and then interact with it in memory. Uploading small amounts of … Read more
A. Introduction to neural networksB. ANN architectures C. Learning methods D. Learning rule on supervised learning E. Feedforward neural network with Gradient descent optimization Introduction to neural networks Definition: the ability to learn, memorize and still generalize, prompted research in algorithmic modeling of biological neural systems. Human brain has the ability to perform tasks such … Read more
Introduction One way to get a diverse set of classifiers is to use very different training algorithms, as just discussed. Another approach is to use the same training algorithm for every predictor, but to train them on different random subsets of the training set. When sampling is performed with replacement, this method is called bagging … Read more
Introduction: Welcome to our comprehensive tutorial on Ensemble Learning! In this guide, we’ll delve into the fascinating world of Ensemble methods, exploring how they harness the collective intelligence of multiple models to achieve superior performance in machine learning tasks. Whether you’re a seasoned practitioner or just stepping into the realm of machine learning, understanding Ensemble … Read more
Decision Trees are also capable of performing regression tasks. Let’s build a regression tree using Scikit-Learn’s DecisionTreeRegressor class, training it on a noisy quadratic dataset with max_depth=2: from sklearn.tree import DecisionTreeRegressor tree_reg = DecisionTreeRegressor(max_depth=2) tree_reg.fit(X, y) The resulting tree is represented below This tree looks very similar to the classification tree you built earlier. The … Read more
By default, the Gini impurity measure is used, but you can select the entropy impurity measure instead by setting the criterion hyperparameter to “entropy”. The concept of entropy originated in thermodynamics as a measure of molecular disorder: entropy approaches zero when molecules are still and well ordered. It later spread to a wide variety of … Read more
Introduction: Linear regression is one of the fundamental techniques in machine learning and statistics used for modeling the relationship between a dependent variable and one or more independent variables. In this tutorial, we’ll delve into the implementation of simple linear regression from scratch using Python. By understanding the mathematical intuition behind linear regression and its … Read more
Like SVMs, Decision Trees are versatile Machine Learning algorithms that can perform both classification and regression tasks, and even multioutput tasks. They are very powerful algorithms, capable of fitting complex datasets. For example, you trained a DecisionTreeRegressor model on the California housing dataset, fitting it perfectly (actually overfitting it).Decision Trees are also the fundamental components … Read more
Introduction As we mentioned earlier, the SVM algorithm is quite versatile: not only does it support linear and nonlinear classification, but it also supports linear and nonlinear regression. The trick is to reverse the objective: instead of trying to fit the largest possible street between two classes while limiting margin violations, SVM Regression tries to … Read more
Introduction In Previous blog we talked about Polynomial Kernel. In this blog we will talk about Gaussian RBF Kernel. Just like the polynomial features method, the similarity features method can be useful with any Machine Learning algorithm, but it may be computationally expensive to compute all the additional features, especially on large training sets. However, … Read more
In Previous blog we talked about Non Linear SVM Classifications. In this blog we will talk about Polynomial Kernel. Introduction Adding polynomial features is simple to implement and can work great with all sorts of Machine Learning algorithms (not just SVMs), but at a low polynomial degree it cannot deal with very complex datasets, and … Read more
Introduction During model development, one of the techniques that many don’t experiment with is feature discretization. The core idea is to transform a continuous feature into discrete features, mostly one-hot encoded. 𝐖𝐡𝐲 𝐰𝐨𝐮𝐥𝐝 𝐰𝐞 𝐝𝐨 𝐭𝐡𝐚𝐭? My rationale for using feature discretization has almost always been simple: “It just makes sense to discretize a feature.” … Read more
In this series we bring new Data Scientist Interview Questions. You can read other tutorials related to same topic on our website. What is Cross Validation? Cross-Validation in Machine Learning is a statistical resampling technique that uses different parts of the dataset to train and test a machine learning algorithm on different iterations. The aim … Read more
In previous blog, We read about Soft margin Classification. In this blog we will talk about Non Linear SVM Classification. Introduction Although linear SVM classifiers are efficient and work surprisingly well in many cases, many datasets are not even close to being linearly separable. One approach to handling nonlinear datasets is to add more features, … Read more
In previous blog, We read about Linear SVM. In this blog we will talk about soft margin classification. If we strictly impose that all instances be off the street and on the right side, this is called hard margin classification. There are two main issues with hard margin classification. First, it only works if the … Read more
In the dynamic world of business, where data-driven decisions reign supreme, the accuracy and reliability of classification models play a pivotal role. Whether you’re involved in lead scoring or any other binary classification system, understanding the intricacies of evaluation metrics is key. Among these, the ROC (Receiver Operating Characteristic) curve and its integral companion, AUC … Read more
A Support Vector Machine (SVM) is a very powerful and versatile Machine Learning model, capable of performing linear or nonlinear classification, regression, and even outlier detection. It is one of the most popular models in Machine Learning, and anyone interested in Machine Learning should have it in their toolbox. SVMs are particularly well suited for … Read more
Machine learning models, particularly those trained iteratively using algorithms like Gradient Descent, face the risk of overfitting the training data. One powerful and elegant solution to this challenge is known as “Early Stopping.” In this blog post, we’ll delve into the concept of Early Stopping, explore its effectiveness, and showcase a practical implementation using a … Read more