Cheat Sheets & Quick Reference

Linear Regression

\[ \hat{y} = \mathbf{w}^T\mathbf{x} + b \qquad \text{MSE} = \frac{1}{m}\sum_i (\hat{y}_i - y_i)^2 \]

Logistic Regression

\[ \sigma(z) = \frac{1}{1+e^{-z}} \qquad \text{Cross-entropy: } -\sum_i [y_i\log\hat{y}_i + (1-y_i)\log(1-\hat{y}_i)] \]

scikit-learn Pipeline

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier

pipe = Pipeline([
    ("scale", StandardScaler()),
    ("clf", RandomForestClassifier(n_estimators=100)),
])
pipe.fit(X_train, y_train)

PyTorch Training Loop (minimal)

for epoch in range(epochs):
    model.train()
    for X_batch, y_batch in loader:
        optimizer.zero_grad()
        loss = criterion(model(X_batch), y_batch)
        loss.backward()
        optimizer.step()

Metric Selection Guide