Sklearn.metrics module model evaluation function

sklearn There is 3 Different species API Used to evaluate the prediction quality of the model .

Estimator scoring method ： The estimator has a scoring method , Provide a default evaluation criteria for the problems they are designed to solve .
Scoring parameters ： Model evaluation tools using cross validation （ Such as model_selection.cross_val_score and model_selection.GridSearchCV） Rely on an internal scoring strategy .
Metric function ：sklearn.metrics The module realizes the function of evaluating the prediction error for a specific purpose .

Example of model evaluation function ：

from sklearn import metrics
#  Check the function of the module 
dir(metrics)

### 1.Accuracy score
import numpy as np
from sklearn.metrics import accuracy_score

y_pred = [0, 2, 1, 3]
y_true = [0, 1, 2, 3]
print(accuracy_score(y_true, y_pred))
print(accuracy_score(y_true, y_pred, normalize=False))  #  The number of correct predictions 

### 2.Top-k accuracy score
# top_k_accuracy_score The function is accuracy_score Generalization .
#  The difference lies in , As long as the real label is consistent with k Associated with one of the highest prediction scores , The prediction is considered to be correct .
#  Accuracy _ The score is k=1 In special circumstances .
import numpy as np
from sklearn.metrics import top_k_accuracy_score
y_true = np.array([0, 1, 2, 2])
y_score = np.array([[0.5, 0.2, 0.2],
                    [0.3, 0.4, 0.2],
                    [0.2, 0.4, 0.3],
                    [0.7, 0.2, 0.1]])
top_k_accuracy_score(y_true, y_score, k=2)

# Not normalizing gives the number of "correctly" classified samples
top_k_accuracy_score(y_true, y_score, k=2, normalize=False)

### 3.confusion_matrix
from sklearn import datasets
from sklearn.svm import LinearSVC
from sklearn.model_selection import cross_validate
from sklearn.metrics import confusion_matrix,ConfusionMatrixDisplay
import matplotlib.pyplot as plt

y_true = [2, 0, 2, 2, 0, 1]
y_pred = [0, 0, 2, 2, 0, 2]

cm=confusion_matrix(y_true, y_pred)
print(confusion_matrix(y_true, y_pred))
print(confusion_matrix(y_true, y_pred,normalize='all'))
disp = ConfusionMatrixDisplay(confusion_matrix=cm)
disp.plot()
plt.show()

#  Two classification 
y_true = [0, 0, 0, 1, 1, 1, 1, 1]
y_pred = [0, 1, 0, 1, 0, 1, 0, 1]
tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()
print(tn, fp, fn, tp)

# A sample toy binary classification dataset
X, y = datasets.make_classification(n_classes=2, random_state=0)
svm = LinearSVC(random_state=0)
def confusion_matrix_scorer(clf, X, y):
    y_pred = clf.predict(X)
    cm = confusion_matrix(y, y_pred)
    return {'tn': cm[0, 0], 'fp': cm[0, 1],
            'fn': cm[1, 0], 'tp': cm[1, 1]}
cv_results = cross_validate(svm, X, y, cv=5,
                            scoring=confusion_matrix_scorer)
# Getting the test set true positive scores
print(cv_results['test_tp'])

# Getting the test set false negative scores
print(cv_results['test_fn'])

print(cv_results['test_tn'])
print(cv_results['test_fp'])

### 4.classification_report
from sklearn.metrics import classification_report
y_true = [0, 1, 2, 2, 0]
y_pred = [0, 0, 2, 1, 0]
target_names = ['class 0', 'class 1', 'class 2']
print(classification_report(y_true, y_pred, target_names=target_names))

### 5. hamming_loss
from sklearn.metrics import hamming_loss
y_pred = [1, 2, 3, 4]
y_true = [2, 2, 3, 4]
hamming_loss(y_true, y_pred)

### 6. Precision, recall and F-measures
from sklearn import metrics
y_pred = [0, 1, 0, 0]
y_true = [0, 1, 0, 1]
metrics.precision_score(y_true, y_pred)
metrics.recall_score(y_true, y_pred)
metrics.f1_score(y_true, y_pred)
metrics.fbeta_score(y_true, y_pred, beta=0.5)
metrics.fbeta_score(y_true, y_pred, beta=1)
metrics.fbeta_score(y_true, y_pred, beta=2)
metrics.precision_recall_fscore_support(y_true, y_pred, beta=0.5)

import numpy as np
from sklearn.metrics import precision_recall_curve
from sklearn.metrics import average_precision_score
y_true = np.array([0, 0, 1, 1])
y_scores = np.array([0.1, 0.4, 0.35, 0.8])
precision, recall, threshold = precision_recall_curve(y_true, y_scores)
print(precision)
print(recall)
print(threshold)
print( average_precision_score(y_true, y_scores))

##  Many classification 
from sklearn import metrics
y_true = [0, 1, 2, 0, 1, 2]
y_pred = [0, 2, 1, 0, 0, 1]
print(metrics.precision_score(y_true, y_pred, average='macro'))
print(metrics.recall_score(y_true, y_pred, average='micro'))
print(metrics.f1_score(y_true, y_pred, average='weighted'))
print(metrics.fbeta_score(y_true, y_pred, average='macro', beta=0.5))
print(metrics.precision_recall_fscore_support(y_true, y_pred, beta=0.5, average=None))

### 7.  Regression prediction r2_score 
# r2_score Function to calculate the coefficient of determination , Usually expressed as R².
#  It represents the variance explained by the independent variables in the model （Y） The proportion of . It provides an indication of the degree of fit ,
#  Therefore, by explaining the proportion of variance, we can measure the degree to which unseen samples may be predicted by the model .

from sklearn.metrics import r2_score
y_true = [3, -0.5, 2, 7]
y_pred = [2.5, 0.0, 2, 8]
r2_score(y_true, y_pred)

### 8.  Regression prediction mean_absolute_error
from sklearn.metrics import mean_absolute_error
y_true = [3, -0.5, 2, 7]
y_pred = [2.5, 0.0, 2, 8]
mean_absolute_error(y_true, y_pred)

### 9.  Regression prediction mean_squared_error 
from sklearn.metrics import mean_squared_error
y_true = [3, -0.5, 2, 7]
y_pred = [2.5, 0.0, 2, 8]
mean_squared_error(y_true, y_pred)

### 10.  Regression prediction mean_squared_log_error
from sklearn.metrics import mean_squared_log_error
y_true = [3, 5, 2.5, 7]
y_pred = [2.5, 5, 4, 8]
mean_squared_log_error(y_true, y_pred)

### 11. Unsupervised clustering Silhouette Coefficient
from sklearn import metrics
from sklearn import datasets
import numpy as np
X, y = datasets.load_iris(return_X_y=True)
 
from sklearn.cluster import KMeans
kmeans_model = KMeans(n_clusters=3, random_state=1).fit(X)
labels = kmeans_model.labels_
metrics.silhouette_score(X, labels, metric='euclidean')

Reference resources ：

https://scikit-learn.org/stable/modules/model_evaluation.html#multilabel-ranking-metrics

https://scikit-learn.org/stable/modules/clustering.html#clustering-evaluation