Decision Trees in Python
Learning Outcomes
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What’s Included
Course Introduction
Course Introduction
In this lesson, we get a preview of the course ahead.
How Decision Trees Work
How Decision Trees Work
In this lesson, we learn about the logic behind decision trees.
Visualizing the Response Feature
Visualizing the Response Feature
In this lesson, we learn how to visualize the response feature in order to see if there's an imbalance of values.
Visualizing the Predictor Features
Visualizing the Predictor Features
In this lesson, we learn how to visualize the predictor features in order to see if there is any noticeable imbalance in the values.
Creating Dummy Variables
Creating Dummy Variables
In this lesson, we learn how to convert our categorical features into numerical features.
Splitting the Data
Splitting the Data
In this lesson, we learn how to split our data into training and testing data.
Training the Model
Training the Model
In this lesson, we learn how to train the decision tree model in Python.
Evaluating Precision
Evaluating Precision
In this lesson, we learn about the precision metric.
Evaluating Recall
Evaluating Recall
In this lesson, we learn about the recall metric.
Visualizing the Decision Tree
Visualizing the Decision Tree
In this lesson, we learn how to visualize the decision tree using Graphviz.
Pruning the Decision Tree
Pruning the Decision Tree
In this lesson, we learn how to reduce the complexity of the decision tree graphic by pruning it.
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