Cross-validation for R-squared

Cross-validation is a vital approach to evaluating a model. It maximizes the amount of data that is available to the model, as the model is not only trained but also tested on all of the available data.

In this exercise, you will build a linear regression model, then use 6-fold cross-validation to assess its accuracy for predicting sales using social media advertising expenditure. You will display the individual score for each of the six-folds.

The sales_df dataset has been split into y for the target variable, and X for the features, and preloaded for you. LinearRegression has been imported from sklearn.linear_model.

This exercise is part of the course

Supervised Learning with scikit-learn

Exercise instructions

Import KFold and cross_val_score.
Create kf by calling KFold(), setting the number of splits to six, shuffle to True, and setting a seed of 5.
Perform cross-validation using reg on X and y, passing kf to cv.
Print the cv_scores.

Hands-on interactive exercise

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

# Import the necessary modules
from ____.____ import ____, ____

# Create a KFold object
kf = ____(n_splits=____, shuffle=____, random_state=____)

reg = LinearRegression()

# Compute 6-fold cross-validation scores
cv_scores = ____(____, ____, ____, cv=____)

# Print scores
print(____)

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

Current Exercise

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 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