Feature extraction

Riduzione della dimensionalità in Python

Jeroen Boeye

Head of Machine Learning, Faktion

Feature selection

Feature selection

Feature extraction

Feature generation - BMI

df_body['BMI'] = df_body['Weight kg'] /  df_body['Height m'] ** 2

Feature generation - BMI

df_body['BMI'] = df_body['Weight kg'] /  df_body['Height m'] ** 2

Weight kg	Height m	BMI
81.5	1.776	25.84
72.6	1.702	25.06
92.9	1.735	30.86

Feature generation - BMI

df_body.drop(['Weight kg', 'Height m'], axis=1)

BMI
25.84
25.06
30.86

Feature generation - averages

left leg mm	right leg mm
882	885
870	869
901	900

leg_df['leg mm'] = leg_df[['right leg mm', 'left leg mm']].mean(axis=1)

Feature generation - averages

leg_df.drop(['right leg mm', 'left leg mm'], axis=1)

leg mm
883.5
869.5
900.5

Cost of taking the average

Cost of taking the average

Cost of taking the average

Cost of taking the average

Intro to PCA

sns.scatterplot(data=df, x='handlength', y='footlength')

Intro to PCA

scaler = StandardScaler()
df_std = pd.DataFrame(scaler.fit_transform(df), columns = df.columns)

Intro to PCA

scaler = StandardScaler()
df_std = pd.DataFrame(scaler.fit_transform(df), columns = df.columns)

Intro to PCA

scaler = StandardScaler()
df_std = pd.DataFrame(scaler.fit_transform(df), columns = df.columns)

Intro to PCA

scaler = StandardScaler()
df_std = pd.DataFrame(scaler.fit_transform(df), columns = df.columns)

Let's practice!

Riduzione della dimensionalità in Python