What is the feature importance in decision tree regressor

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Reference no: EM133676866

Homework: Data Science

In this homework, you will be required to do research about Decision tree Regressor and other common regressions such as Ridge Regression, Lasso Regression, and Logistic Regression.

You will need to do the research about these regression models, but you don't need to find their mathematical formulas. You are only required to know what they are, how and when to use them.

I. Research

You will need to find the answers for the following questions in order to help you understand how these regressions work. Write your answers for each question in your write-ups.

1. What is Decision tree Regressor?
2. What is the difference between Decision Tree Regressor and Decision Tree Classifier.
3. What is the feature importance in Decision Tree Regressor?
4. What is Ridge Regression?
5. What is Lasso Regression?
6. What is Logistic Regression?

II. Use the Boston housing data again. Since we have done EDA of this dataset in homework, it will save us a lot of time so that we can focus on applying each regression that we discussed above.

1. For Linear Regression, Ridge Regression, Lass Regression, and Logistic Regression, find the correlations for all the independent variables and dependent variables. Select the feature variables that correlate to the price of the house. (To use the logistics model, you may have to separate the price of the house into low, medium, and high).

2. For Decision Tree Regressor, we will use all features to predict the price of the Boston house price.

3. Apply Linear Regression, Ridge Regression, Lass Regression, Logistic Regression, and Decision Tree Regressor to the data. Your homework should have at least 5 models.

4. Comparing the MSE, RMSE, and its accuracies.

5. Choose the model(s) that you think appropriate and predict the house price.

6. Only for Decision Tree Regressor, do the tree visualization, and plot the feature importance, find which feature has the highest importance, and which feature is the second highest importance.

7. Interpret the results.

Reference no: EM133676866

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