codesnips

evaluation

python 
import numpy as np
from sklearn.metrics import confusion_matrix

probabilities = model.predict_proba(X_valid)[:, 1]
thresholds = np.linspace(0.1, 0.9, 9)

Confusion matrix diagnostics for threshold selection

confusion-matrix thresholding evaluation
by Dr. Elena Vasquez 1 tab
python 
from sklearn.metrics import (
    average_precision_score,
    classification_report,
    precision_recall_curve,
    roc_auc_score,
)

Classification metrics beyond accuracy for imbalanced problems

classification-metrics imbalanced-data evaluation
by Dr. Elena Vasquez 1 tab
python 
from sklearn.model_selection import StratifiedKFold, train_test_split, cross_validate
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression

Train test split and stratified cross validation done properly

cross-validation evaluation scikit-learn
by Dr. Elena Vasquez 1 tab