Model evaluation ensures that your machine learning model performs well on unseen data. It helps detect overfitting, underfitting, and model bias.
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
from sklearn.model_selection import KFold
kf = KFold(n_splits=5)
from sklearn.metrics import confusion_matrix
confusion_matrix(y_test, predictions)
Model evaluation is a key step in machine learning. It ensures that your model generalizes well and performs accurately in real-world environments.
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