python_生成分類器評估名額報告

生成分類器評估名額報告

# 生成評估名額報告
from sklearn import datasets
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report

# load data
iris = datasets.load_iris()

# create feature matrix
features = iris.data

# create target vector
target = iris.target

# 建立目标分類嗎清單
class_names = iris.target_names

# create training and test set
# 建立訓練集和測試集
features_train, features_test, target_train, target_test = train_test_split(features, target, random_state=1)

# create logistic regression
# 建立邏輯回歸對象
classifier = LogisticRegression()

# train model and make predictions
# 訓練模型并預測
model = classifier.fit(features_train, target_train)
target_predicted = model.predict(features_test)

# create classification report
生成分類器性能名額
print(classification_report(target_test, target_predicted, target_names=class_names))
              precision    recall  f1-score   support

      setosa       1.00      1.00      1.00        13
  versicolor       1.00      0.94      0.97        16
   virginica       0.90      1.00      0.95         9

    accuracy                           0.97        38
   macro avg       0.97      0.98      0.97        38
weighted avg       0.98      0.97      0.97        38