IG( Y, X) = Entropy (Y) - Entropy ( Y | X)ģ. Information Gain: The information gain measures the decrease in entropy after the data set is split. It is calculated using the following formula:Ģ. Entropy handles how a decision tree splits the data. Entropy: Entropy is the measure of uncertainty or randomness in a data set. Decision trees can also be used to find customer churn rates.ġ.It can help ecommerce companies in predicting whether a consumer is likely to purchase a specific product.It can be used to determine the odds of an individual developing a specific disease.A decision tree is used to determine whether an applicant is likely to default on a loan.Supervised Machine Learning: All You Need to Know Lesson - 33 Top 45 Machine Learning Interview Questions and Answers for 2023 Lesson - 31Įxplaining the Concepts of Quantum Computing Lesson - 32 How to Become a Machine Learning Engineer? Lesson - 30 Mathematics for Machine Learning - Important Skills You Must Possess Lesson - 27Ī One-Stop Guide to Statistics for Machine Learning Lesson - 28Įmbarking on a Machine Learning Career? Here’s All You Need to Know Lesson - 29 The Complete Guide on Overfitting and Underfitting in Machine Learning Lesson - 26 The Best Guide to Regularization in Machine Learning Lesson - 24Įverything You Need to Know About Bias and Variance Lesson - 25 What Is Q-Learning? The Best Guide to Understand Q-Learning Lesson - 23 What Is Reinforcement Learning? The Best Guide To Reinforcement Learning Lesson - 22 The Ultimate Guide to Cross-Validation in Machine Learning Lesson - 20Īn Easy Guide to Stock Price Prediction Using Machine Learning Lesson - 21 What is Cost Function in Machine Learning Lesson - 19 PCA in Machine Learning: Your Complete Guide to Principal Component Analysis Lesson - 18 K-Means Clustering Algorithm: Applications, Types, Demos and Use Cases Lesson - 17 How to Leverage KNN Algorithm in Machine Learning? Lesson - 16 The Best Guide to Confusion Matrix Lesson - 15 Understanding Naive Bayes Classifier Lesson - 14 The Best Guide On How To Implement Decision Tree In Python Lesson - 12 Understanding the Difference Between Linear vs. Supervised and Unsupervised Learning in Machine Learning Lesson - 6Įverything You Need to Know About Feature Selection Lesson - 7Įverything You Need to Know About Classification in Machine Learning Lesson - 9Īn Introduction to Logistic Regression in Python Lesson - 10 Top 10 Machine Learning Applications in 2023 Lesson - 4Īn Introduction to the Types Of Machine Learning Lesson - 5 Machine Learning Steps: A Complete Guide Lesson - 3 What is Machine Learning and How Does It Work? Lesson - 2 An Introduction To Machine Learning Lesson - 1
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