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Supervised Learning with scikit-learn
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• ## .mfe-app-workspace-kj242g{position:absolute;top:-8px;}.mfe-app-workspace-11ezf91{display:inline-block;}.mfe-app-workspace-11ezf91:hover .Anchor__copyLink{visibility:visible;}Supervised Learning with scikit-learn

Run the hidden code cell below to import the data used in this course.

### Take Notes

Add notes about the concepts you've learned and code cells with code you want to keep.

Create a cross validation

```.mfe-app-workspace-11z5vno{font-family:JetBrainsMonoNL,Menlo,Monaco,'Courier New',monospace;font-size:13px;line-height:20px;}```# Add your # Import the necessary modules
from sklearn.model_selection import KFold, cross_val_score

# Create a KFold object
kf = KFold(n_splits=6, shuffle=True, random_state=5)

reg = LinearRegression()

# Compute 6-fold cross-validation scores
cv_scores = cross_val_score(reg, X, y,cv=kf)

# Print scores
print(cv_scores)code snippets here``````

Generating metrics

``````# Print the mean
print(np.mean(cv_results))

# Print the standard deviation
print(np.std(cv_results))

# Print the 95% confidence interval
print(np.quantile(cv_results, [0.025, 0.975]))``````

Regularization with Ridge

``````# Import Ridge
from sklearn.linear_model import Ridge
alphas = [0.1, 1.0, 10.0, 100.0, 1000.0, 10000.0]
ridge_scores = []
for alpha in alphas:

# Create a Ridge regression model
ridge = Ridge(alpha=alpha)

# Fit the data
ridge.fit(X_train,y_train)

# Obtain R-squared
score = ridge.score(X_test, y_test)
ridge_scores.append(score)
print(ridge_scores)``````

Regularization with Lasso

``````# Import Lasso
from sklearn.linear_model import Lasso

# Instantiate a lasso regression model
lasso = Lasso(alpha=0.3)

# Fit the model to the data
lasso.fit(X,y)

# Compute and print the coefficients
lasso_coef = lasso.coef_
print(lasso_coef)
plt.bar(sales_columns, lasso_coef)
plt.xticks(rotation=45)
plt.show()``````