# # Select and train a model
# In[80]:
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(housing_prepared, housing_labels)
# In[81]:
# let's try the full preprocessing pipeline on a few training instances
some_data = housing.iloc[:5]
some_labels = housing_labels.iloc[:5]
some_data_prepared = full_pipeline.transform(some_data)
print("Predictions:", lin_reg.predict(some_data_prepared))
# Compare against the actual values:
# In[82]:
print("Labels:", list(some_labels))
# In[83]:
some_data_prepared
# In[84]:
#%%
cvres = rnd_search.cv_results_
for mean_score, params in zip(cvres['mean_test_score'], cvres['params']):
print(np.sqrt(-mean_score), params)
#%%
feature_importances = grid_search.best_estimator_.feature_importances_
#%%
final_model = grid_search.best_estimator_
X_test = strat_test_set.drop('median_house_value', axis=1)
y_test = strat_test_set['median_house_value'].copy()
X_test_prepared = full_pipeline.transform(X_test)
final_predictions = final_model.predict(X_test_prepared)
final_mse = mean_squared_error(y_test, final_predictions)
final_rmse = np.sqrt(final_mse)
#%% Exercise 1
from sklearn.svm import SVR
svm_reg = SVR()
param_grid = [{
'kernel': ['linear'],
'C': [10.0, 1000., 1000.0, 10000.0]
}, {
'kernel': ['rbf'],
'C': [1.0, 10.0, 100.0, 1000.0],