What is Stacking of Models in Machine Learning?
The last Ensemble method we will discuss in this series is called stacking (short for stacked generalization). It is based on a simple idea: instead of using trivial functions (such…
Gradient Boosting in Machine Learning
Another very popular Boosting algorithm is Gradient Boosting. Just like AdaBoost,Gradient Boosting works by sequentially adding predictors to an ensemble, each one correcting its predecessor. However, instead of tweaking the…
Boosting( Ensemble) Trees | Machine Learning from Scratch
Introduction Boosting (originally called hypothesis boosting) refers to any Ensemble method that can combine several weak learners into a strong learner. The general idea of most boosting methods is to…
What is Bagging and Pasting? Machine Learning from Scratch
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…
What is Ensemble Learning? | Machine Learning from Scratch
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…
Decision Tree Regression | Machine Learning from Scratch
Decision Trees are also capable of performing regression tasks. Letโs build a regression tree using Scikit-Learnโs DecisionTreeRegressor class, training it on a noisy quadratic dataset with max_depth=2: from sklearn.tree import…
Gini Impurity or Entropy? How to decide the root node in decision tree?
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…
Decision Trees | Machine Learning from Scratch
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…
ROC and AUC in Evaluating Classification Models
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…
Information Gain in Machine Learning
Information Gain (IG) is critical in machine learning and decision tree algorithms, particularly in data classification and ๐๐๐๐ญ๐ฎ๐ซ๐ ๐ฌ๐๐ฅ๐๐๐ญ๐ข๐จ๐ง. Information Gain Information Gain is a concept used in the field…
Learning Curves | Machine Learning from Scratch
Till now, We have read about Gradient Descent,Min-Batch Gradient Descent,Stochastic Gradient Descent and other type of Gradient Descents and Polynomial Regression. In this post we will learn about Learning Curves…