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  3. Machine Learning for Finance in Python
  • 1

    Preparing data and a linear model

    Free

    In this chapter, we will learn how machine learning can be used in finance. We will also explore some stock data, and prepare it for machine learning algorithms. Finally, we will fit our first machine learning model -- a linear model, in order to predict future price changes of stocks.

  • 2

    Machine learning tree methods

    Learn how to use tree-based machine learning models to predict future values of a stock's price, as well as how to use forest-based machine learning methods for regression and feature selection.

  • 3

    Neural networks and KNN

    We will learn how to normalize and scale data for use in KNN and neural network methods. Then we will learn how to use KNN and neural network regression to predict the future values of a stock's price (or any other regression problem).

  • 4

    Machine learning with modern portfolio theory

    In this chapter, you'll learn how to use modern portfolio theory (MPT) and the Sharpe ratio to plot and find optimal stock portfolios. You'll also use machine learning to predict the best portfolios. Finally, you'll evaluate performance of the ML-predicted portfolios.

Initializing

Exercise

Try different max depths

We always want to optimize our machine learning models to make the best predictions possible. We can do this by tuning hyperparameters, which are settings for our models. We will see in more detail how these are useful in future chapters, but for now think of them as knobs we can turn to tune our predictions to be as good as possible.

For regular decision trees, probably the most important hyperparameter is max_depth. This limits the number of splits in a decision tree. Let's find the best value of max_depth based on the R\(^2\) score of our model on the test set, which we can obtain using the score() method of our decision tree models.

Instructions

100 XP
  • Loop through the values 3, 5, and 10 for use as the max_depth parameter in our decision tree model.
  • Set the max_depth parameter in our DecisionTreeRegressor to be equal to d in each loop iteration.
  • Print the model's score on the train_features and train_targets.