Lokale Validierung

Eine Kaggle-Competition in Python gewinnen

Yauhen Babakhin

Kaggle Grandmaster

Motivation

Overfitting-Beispiel mit Public- und Private-Leaderboards

Holdout-Set

Schema Holdout-Set

Holdout-Set

Schema Holdout-Set

Holdout-Set

Schema Holdout-Set

K-Fold-Cross-Validation

Train-Daten in vier Folds aufteilen

K-Fold-Cross-Validation

Schema der K-Fold-Cross-Validation

K-Fold-Cross-Validation

# Import KFold
from sklearn.model_selection import KFold

# Create a KFold object
kf = KFold(n_splits=5, shuffle=True, random_state=123)

# Loop through each cross-validation split
for train_index, test_index in kf.split(train):

    # Get training and testing data for the corresponding split
    cv_train, cv_test = train.iloc[train_index], train.iloc[test_index]

Stratifiziertes K-Fold

Schema der stratifizierten K-Fold-Cross-Validation

Stratifiziertes K-Fold

# Import StratifiedKFold
from sklearn.model_selection import StratifiedKFold


# Create a StratifiedKFold object
str_kf = StratifiedKFold(n_splits=5, shuffle=True, random_state=123)


# Loop through each cross-validation split
for train_index, test_index in str_kf.split(train, train['target']):
    cv_train, cv_test = train.iloc[train_index], train.iloc[test_index]

Lass uns üben!

Eine Kaggle-Competition in Python gewinnen