Varianz im maschinellen Lernen
from mlxtend.evaluate import bias_variance_decomp
from sklearn.tree import DecisionTreeClassifier
from mlxtend.data import iris_data
from sklearn.model_selection import train_test_split
# Get Data Set
X, y = iris_data()
X_train_ds, X_test_ds, y_train_ds, y_test_ds = train_test_split(X, y,
test_size=0.3,
random_state=123,
shuffle=True,
stratify=y)
# Define Algorithm
tree = DecisionTreeClassifier(random_state=123)
# Get Bias and Variance - bias_variance_decomp function
avg_expected_loss, avg_bias, avg_var = bias_variance_decomp(
tree, X_train_ds, y_train_ds, X_test_ds, y_test_ds,
loss='0-1_loss',
random_seed=123,
num_rounds=1000)
# Display Bias and Variance
print(f'Average Expected Loss: {round(avg_expected_loss, 4)}n')
print(f'Average Bias: {round(avg_bias, 4)}')
print(f'Average Variance: {round(avg_var, 4)}')
Json Bourne
