Machine Learning With Python 简明教程
Machine Learning - Performance Metrics
机器学习中的性能指标用于评估机器学习模型的性能。这些指标提供量化措施，以评估模型的执行情况以及比较不同模型的性能。性能指标很重要，因为它们帮助我们了解模型的执行情况以及它是否满足我们的要求。通过这种方式，我们可以就是否使用特定模型做出明智的决定。
Performance metrics in machine learning are used to evaluate the performance of a machine learning model. These metrics provide quantitative measures to assess how well a model is performing and to compare the performance of different models. Performance metrics are important because they help us understand how well our model is performing and whether it is meeting our requirements. In this way, we can make informed decisions about whether to use a particular model or not.
机器学习中可以使用许多性能指标，具体取决于所解决的问题的类型以及问题的具体要求。一些常见的性能指标包括 −
There are many performance metrics that can be used in machine learning, depending on the type of problem being solved and the specific requirements of the problem. Some common performance metrics include −
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Accuracy − Accuracy is one of the most basic performance metrics and measures the proportion of correctly classified instances in the dataset. It is calculated as the number of correctly classified instances divided by the total number of instances in the dataset.
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Precision − Precision measures the proportion of true positive instances out of all predicted positive instances. It is calculated as the number of true positive instances divided by the sum of true positive and false positive instances.
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Recall − Recall measures the proportion of true positive instances out of all actual positive instances. It is calculated as the number of true positive instances divided by the sum of true positive and false negative instances.
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F1 Score − F1 score is the harmonic mean of precision and recall. It is a balanced measure that takes into account both precision and recall. It is calculated as 2 * (precision × recall) / (precision + recall).
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ROC AUC Score − ROC AUC (Receiver Operating Characteristic Area Under the Curve) score is a measure of the ability of a classifier to distinguish between positive and negative instances. It is calculated by plotting the true positive rate against the false positive rate at different classification thresholds and calculating the area under the curve.
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Confusion Matrix − A confusion matrix is a table that is used to evaluate the performance of a classification model. It shows the number of true positives, true negatives, false positives, and false negatives for each class in the dataset.
Example
以下是用于计算二进制分类问题的准确率、精确率、召回率和 F1 值的示例代码片段 −
Here is an example code snippet to calculate the accuracy, precision, recall, and F1 score for a binary classification problem −
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
# Load the iris dataset
iris = load_iris()
X = iris.data
y = iris.target
# Split the dataset into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Train a logistic regression model on the training set
model = LogisticRegression()
model.fit(X_train, y_train)
# Make predictions on the test set
y_pred = model.predict(X_test)
# Compute performance metrics
accuracy = accuracy_score(y_test, y_pred)
precision = precision_score(y_test, y_pred, average='macro')
recall = recall_score(y_test, y_pred, average='macro')
f1 = f1_score(y_test, y_pred, average='macro')
# Print the performance metrics
print("Accuracy:", accuracy)
print("Precision:", precision)
print("Recall:", recall)
print("F1 Score:", f1)