Gradient boosting

Ensemble Methods in Python

Román de las Heras

Data Scientist, Appodeal

Intro to gradient boosting machine

function_objective.png

  1. Initial model (weak estimator): $y\sim f_1(X)$
  2. New model fits to residuals: $y-f_1(X)\sim f_2(X)$
  3. New additive model: $y\sim f_1(X)+f_2(X)$
  4. Repeat $n$ times or until error is small enough
  5. Final additive model: $$y\sim f_1(X)+f_2(X)+ ... +f_n(x)=\sum_{i=1}^{n} f_i(X)$$
Ensemble Methods in Python

Equivalence to gradient descent

function_residuals.png

Gradient Descent:

function_loss.png

function_gradient.png

Residuals = Negative Gradient

function_gradient_negative.png

Ensemble Methods in Python

Gradient boosting classifier

Gradient Boosting Classifier

from sklearn.ensemble import GradientBoostingClassifier

clf_gbm = GradientBoostingClassifier(
   n_estimators=100,
   learning_rate=0.1,
   max_depth=3,
   min_samples_split,
   min_samples_leaf,
   max_features
)
  • n_estimators
    • Default: 100
  • learning_rate
    • Default: 0.1
  • max_depth
    • Default: 3
  • min_samples_split
  • min_samples_leaf
  • max_features
Ensemble Methods in Python

Gradient boosting regressor

Gradient Boosting Regressor

from sklearn.ensemble import GradientBoostingRegressor

reg_gbm = GradientBoostingRegressor(
   n_estimators=100,
   learning_rate=0.1,
   max_depth=3,
   min_samples_split,
   min_samples_leaf,
   max_features
)
Ensemble Methods in Python

Time to boost!

Ensemble Methods in Python

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