How to use Classification and Regression Tree (CART) in Python
Classification and Regression Trees (CART) is a popular method of supervised machine learning that uses a tree-based model to make predictions. It is used for both classification and regression problems, hence the name. In this article, we will go over the basics of how to use CART in Python.
First, we need to import the necessary libraries such as Numpy and Pandas, which will help us handle our data. Next, we will import the DecisionTreeClassifier or DecisionTreeRegressor class from the sklearn.tree library, which will be used to create our tree-based model.
Once we have our libraries and classes imported, we can start creating our model. To do this, we will first need to load our data into a Pandas dataframe. We can do this by using the read_csv function, which will allow us to read in data from a CSV file.
Once our data is loaded, we will need to split it into training and testing sets. This is important because it allows us to test the accuracy of our model on unseen data. We can do this using the train_test_split function, which will randomly split our data into training and testing sets.
Now that our data is ready, we can create our model. We do this by instantiating the DecisionTreeClassifier or DecisionTreeRegressor class and then fitting it to our training data using the fit method. Once the model is trained, we can use it to make predictions on our testing data using the predict method.
To check the accuracy of our model, we can use different metrics such as accuracy score, precision, recall, and f1-score for classification and R2 score, mean squared error (MSE) for regression.
Lastly, we need to optimise our model. One way to do this is by tuning the model’s parameters. The most important parameter is the maximum depth of the tree, which controls how deep the tree can grow. We can use a grid search to find the best maximum depth for our data.
In conclusion, using CART in Python is a straightforward process. By using the sklearn.tree library, we can easily load and train our data using the DecisionTreeClassifier or DecisionTreeRegressor class. By tuning the maximum depth of the tree and using different metrics, we can optimise the accuracy of our model and make more accurate predictions.
In this Machine Learning Recipe, you will learn: How to use Classification and Regression Tree (CART) in Python.
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