cross-validation

import pandas as pd

# Read the data
data = pd.read_csv('../input/melbourne-housing-snapshot/melb_data.csv')

# Select subset of predictors
cols_to_use = ['Rooms', 'Distance', 'Landsize', 'BuildingArea', 'YearBuilt']
X = data[cols_to_use]

# Select target
y = data.Price
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  %Then, we define a pipeline that uses an imputer to fill in missing values and a random forest model to make predictions.
  %While it's possible to do cross-validation without pipelines, it is quite difficult! Using a pipeline will make the code remarkably straightforward.

from sklearn.ensemble import RandomForestRegressor
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer

my_pipeline = Pipeline(steps=[('preprocessor', SimpleImputer()),
                              ('model', RandomForestRegressor(n_estimators=50, random_state=0))
                             ])
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  %We obtain the cross-validation scores with the cross_val_score() function from scikit-learn. We set the number of folds with the cv parameter.
 
from sklearn.model_selection import cross_val_score

# Multiply by -1 since sklearn calculates *negative* MAE
scores = -1 * cross_val_score(my_pipeline, X, y,
                              cv=5,
                              scoring='neg_mean_absolute_error')

print("MAE scores:\n", scores)
print("Average MAE score (across experiments):")
print(scores.mean())