Decision Trees & Stock Market Analysis Predictions | Machine Learning with TensorFlow & scikit-learn

Let’s reach 100K subscribers 👉🏻 📚About This lecture elaborates on decision trees for classification and regression tasks. We discuss the Classification And Regression Tree, CART, algorithm. In a classification approach, we discuss two costs that are minimized in a greedy manner using the sub-optimal CART algorithm, which are the Gini impurity and the Entropy. In a regression approach, this cost is simply replaced by the mean-squared error (MSE). Furthermore, we discuss the sub-optimality of the CART algorithm, which does not necessarily yield the optimal tree. On the other hand, obtaining the optimal tree is an NP-complete problem. The other part of the lecture is dedicated for python implementations of both Decision Tree classifiers and regressors. We also show how to plot the tree on python using graphviz. Finally, we end the lecture with a Stock Market Analysis case study with the intent of predicting buy or sell signals. We study different sto