Regression with categorical features

Now you have created music_dummies, containing binary features for each song's genre, it's time to build a ridge regression model to predict song popularity.

music_dummies has been preloaded for you, along with Ridge, cross_val_score, numpy as np, and a KFold object stored as kf.

The model will be evaluated by calculating the average RMSE, but first, you will need to convert the scores for each fold to positive values and take their square root. This metric shows the average error of our model's predictions, so it can be compared against the standard deviation of the target value—"popularity".

This exercise is part of the course

Supervised Learning with scikit-learn

Exercise instructions

Create X, containing all features in music_dummies, and y, consisting of the "popularity" column, respectively.
Instantiate a ridge regression model, setting alpha equal to 0.2.
Perform cross-validation on X and y using the ridge model, setting cv equal to kf, and using negative mean squared error as the scoring metric.
Print the RMSE values by converting negative scores to positive and taking the square root.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Create X and y
X = ____
y = ____

# Instantiate a ridge model
ridge = ____

# Perform cross-validation
scores = ____(____, ____, ____, cv=____, scoring="____")

# Calculate RMSE
rmse = np.____(____)
print("Average RMSE: {}".format(np.mean(rmse)))
print("Standard Deviation of the target array: {}".format(np.std(y)))

Edit and Run Code

This exercise is part of the course

Supervised Learning with scikit-learn

IntermediateSkill Level

4.8+

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In this chapter, you'll be introduced to classification problems and learn how to solve them using supervised learning techniques. You'll learn how to split data into training and test sets, fit a model, make predictions, and evaluate accuracy. You’ll discover the relationship between model complexity and performance, applying what you learn to a churn dataset, where you will classify the churn status of a telecom company's customers.

Exercise 1: Machine learning with scikit-learn Exercise 2: Binary classification Exercise 3: The supervised learning workflow Exercise 4: The classification challenge Exercise 5: k-Nearest Neighbors: Fit Exercise 6: k-Nearest Neighbors: Predict Exercise 7: Measuring model performance Exercise 8: Train/test split + computing accuracy Exercise 9: Overfitting and underfitting Exercise 10: Visualizing model complexity

In this chapter, you will be introduced to regression, and build models to predict sales values using a dataset on advertising expenditure. You will learn about the mechanics of linear regression and common performance metrics such as R-squared and root mean squared error. You will perform k-fold cross-validation, and apply regularization to regression models to reduce the risk of overfitting.

Exercise 1: Introduction to regression Exercise 2: Creating features Exercise 3: Building a linear regression model Exercise 4: Visualizing a linear regression model Exercise 5: The basics of linear regression Exercise 6: Fit and predict for regression Exercise 7: Regression performance Exercise 8: Cross-validation Exercise 9: Cross-validation for R-squared Exercise 10: Analyzing cross-validation metrics Exercise 11: Regularized regression Exercise 12: Regularized regression: Ridge Exercise 13: Lasso regression for feature importance

Having trained models, now you will learn how to evaluate them. In this chapter, you will be introduced to several metrics along with a visualization technique for analyzing classification model performance using scikit-learn. You will also learn how to optimize classification and regression models through the use of hyperparameter tuning.

Exercise 1: How good is your model?Exercise 2: Deciding on a primary metric Exercise 3: Assessing a diabetes prediction classifier Exercise 4: Logistic regression and the ROC curve Exercise 5: Building a logistic regression model Exercise 6: The ROC curve Exercise 7: ROC AUC Exercise 8: Hyperparameter tuning Exercise 9: Hyperparameter tuning with GridSearchCV Exercise 10: Hyperparameter tuning with RandomizedSearchCV

Learn how to impute missing values, convert categorical data to numeric values, scale data, evaluate multiple supervised learning models simultaneously, and build pipelines to streamline your workflow!

Exercise 1: Preprocessing data Exercise 2: Creating dummy variables Exercise 3: Regression with categorical features

Current Exercise

Exercise 4: Handling missing data Exercise 5: Dropping missing data Exercise 6: Pipeline for song genre prediction: I Exercise 7: Pipeline for song genre prediction: II Exercise 8: Centering and scaling Exercise 9: Centering and scaling for regression Exercise 10: Centering and scaling for classification Exercise 11: Evaluating multiple models Exercise 12: Visualizing regression model performance Exercise 13: Predicting on the test set Exercise 14: Visualizing classification model performance Exercise 15: Pipeline for predicting song popularity Exercise 16: Congratulations