Checking for correlated features
You'll now return to the wine dataset, which consists of continuous, numerical features. Run Pearson's correlation coefficient on the dataset to determine which columns are good candidates for eliminating. Then, remove those columns from the DataFrame.
Questo esercizio fa parte del corso
Preprocessing for Machine Learning in Python
Istruzioni dell'esercizio
- Print out the Pearson correlation coefficients for each pair of features in the
winedataset. - Drop any columns from
winethat have a correlation coefficient above 0.75 with at least two other columns.
Esercizio pratico interattivo
Prova a risolvere questo esercizio completando il codice di esempio.
# Print out the column correlations of the wine dataset
print(____)
# Drop that column from the DataFrame
wine = wine.____(____, ____)
print(wine.head())