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  3. Extreme Gradient Boosting with XGBoost
  • 1

    Classification with XGBoost

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    This chapter will introduce you to the fundamental idea behind XGBoost—boosted learners. Once you understand how XGBoost works, you'll apply it to solve a common classification problem found in industry: predicting whether a customer will stop being a customer at some point in the future.

  • 2

    Regression with XGBoost

    After a brief review of supervised regression, you'll apply XGBoost to the regression task of predicting house prices in Ames, Iowa. You'll learn about the two kinds of base learners that XGboost can use as its weak learners, and review how to evaluate the quality of your regression models.

  • 3

    Fine-tuning your XGBoost model

    This chapter will teach you how to make your XGBoost models as performant as possible. You'll learn about the variety of parameters that can be adjusted to alter the behavior of XGBoost and how to tune them efficiently so that you can supercharge the performance of your models.

  • 4

    Using XGBoost in pipelines

    Take your XGBoost skills to the next level by incorporating your models into two end-to-end machine learning pipelines. You'll learn how to tune the most important XGBoost hyperparameters efficiently within a pipeline, and get an introduction to some more advanced preprocessing techniques.

Initializing

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Decision trees

Your task in this exercise is to make a simple decision tree using scikit-learn's DecisionTreeClassifier on the breast cancer dataset that comes pre-loaded with scikit-learn.

This dataset contains numeric measurements of various dimensions of individual tumors (such as perimeter and texture) from breast biopsies and a single outcome value (the tumor is either malignant, or benign).

We've preloaded the dataset of samples (measurements) into X and the target values per tumor into y. Now, you have to split the complete dataset into training and testing sets, and then train a DecisionTreeClassifier. You'll specify a parameter called max_depth. Many other parameters can be modified within this model, and you can check all of them out here.

Anweisungen

100 XP
  • Import:
    • train_test_split from sklearn.model_selection.
    • DecisionTreeClassifier from sklearn.tree.
  • Create training and test sets such that 20% of the data is used for testing. Use a random_state of 123.
  • Instantiate a DecisionTreeClassifier called dt_clf_4 with a max_depth of 4. This parameter specifies the maximum number of successive split points you can have before reaching a leaf node.
  • Fit the classifier to the training set and predict the labels of the test set.