Features & Labels

X and y — the inputs and outputs of every supervised ML model

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Explanation

Features (X) — the input variables the model uses to make predictions. Also called: independent variables, predictors, attributes, inputs.

Labels (y) — the output variable the model predicts. Also called: target, dependent variable, response.

python

import pandas as pd
from sklearn.model_selection import train_test_split

df = pd.read_csv('titanic.csv')

# Features: everything except the target
X = df[['pclass', 'age', 'fare', 'sex_encoded']]

# Label: what we want to predict
y = df['survived']

X_train, X_test, y_train, y_test = train_test_split(X, y)

Feature engineering — creating new features from existing ones: ``python df['family_size'] = df['siblings'] + df['parents'] + 1 df['is_alone'] = (df['family_size'] == 1).astype(int)

Feature importance: Most algorithms can tell you which features most influenced predictions — critical for interpretability.

Curse of dimensionality: Too many features (especially irrelevant ones) can hurt model performance. Feature selection is an important skill.

Examples

Preparing X and y from a DataFrame

feature_importances_ shows which features the model relied on most

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier

df = pd.DataFrame({'age':[25,35,45,28,52],'salary':[40,70,90,45,95],'bought':[0,1,1,0,1]})

X = df[['age', 'salary']]  # features
y = df['bought']            # label

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
model = RandomForestClassifier()
model.fit(X_train, y_train)

# Feature importances
for feat, imp in zip(X.columns, model.feature_importances_):
    print(f"{feat}: {imp:.3f}")

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