Python Tutorial: Decision-Tree for Classification

Want to learn more? Take the full course at https://learn.datacamp.com/courses/machine-learning-with-tree-based-models-in-python at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Hi! My name is Elie Kawerk, I'm a Data Scientist and I'll be your instructor. In this course, you'll be learning about tree-based models for classification and regression. In chapter 1, you'll be introduced to a set of supervised learning models known as Classification-And-Regression-Tree or CART. In chapter 2, you'll understand the notions of bias-variance trade-off and model ensembling. Chapter 3 introduces you to Bagging and Random Forests. Chapter 4 deals with boosting, specifically with AdaBoost and Gradient Boosting. Finally, in chapter 5, you'll understand how to get the most out of your models through hyperparameter-tuning. Given a labeled dataset, a classification tree learns a sequence of if-else questions about individual features in order to infer the labels. In contrast to linear models, trees are able to capture non-linear relationships between features and labels. In addition, trees don't require the features to be on the same scale through standardization for example. To understand trees more concretely, we'll try to predict whether a tumor is malignant or benign in the Wisconsin Breast Cancer dataset using only 2 features. The figure here shows a scatterplot of two cancerous cell features with malignant-tumors in blue and benign-tumors in red. When a classification tree is trained on this dataset, the tree learns a sequence of if-else questions with each question involving one feature and one split-point. Take a look at the tree diagram here. At the top, the tree asks whether the concave-points mean of an instance is smaller or equal 0-point-051. If it is, the instance traverses the True branch; otherwise, it traverses the False branch. Similarly, the instance keeps traversing the internal branches