Using Sklearn package for Decision Tree

Using Sklearn Package for Decision Tree | Python Machine Learning Tutorial
In this video, we show you how to implement a Decision Tree model using the Sklearn (Scikit-learn) package in Python. Scikit-learn provides an easy-to-use interface for building decision tree classifiers and regressors, making it an essential tool for machine learning practitioners.
Topics covered in this tutorial include:
Introduction to Sklearn: An overview of the Scikit-learn package and its role in building machine learning models.
Setting Up the Environment: Installing and configuring Scikit-learn for decision tree tasks.
Building the Decision Tree Classifier: Step-by-step guide to creating a decision tree classifier using DecisionTreeClassifier from Sklearn.
Training the Model: How to train your decision tree on a dataset and understand key parameters like max_depth, min_samples_split, and criterion (Gini impurity or entropy).
Evaluating the Model: How to evaluate the performance of your decision tree classifier using metrics like accuracy, precision, recall, confusion matrix, and cross-validation.
Pruning and Overfitting: Techniques to avoid overfitting by pruning the tree or limiting the tree's depth.
Visualizing the Decision Tree: How to visualize the decision tree using plot_tree or Graphviz to interpret the splits and decision rules.
Handling Regression Problems: A quick overview of using decision trees for regression tasks with DecisionTreeRegressor and how it differs from classification.
By the end of this video, you’ll have a complete understanding of how to build, evaluate, and interpret decision tree models using Sklearn for both classification and regression tasks.
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