混淆矩阵、准确率、召回率loss值得含义
时间: 2023-10-11 20:14:06 浏览: 50
混淆矩阵是用于评估分类模型性能的表格,其中行表示实际标签,列表示预测标签。混淆矩阵中的四个值分别是:真正(True Positive, TP)、假正(False Positive, FP)、真负(True Negative, TN)、假负(False Negative, FN)。它们表示分类器正确预测样本属于正类或负类的情况。
准确率是指分类器正确预测样本所占比例,即 TP+TN/(TP+FP+TN+FN)。准确率高表示分类器在预测时较为准确。
召回率是指分类器正确预测属于正类的样本数与所有实际正类样本数的比例,即 TP/(TP+FN)。召回率高表示分类器在预测正类时较为全面。
loss值(损失值)是指模型在训练过程中的损失函数值,通常是用来评估模型的拟合程度。训练模型的目标是尽量减小损失函数的值。在分类问题中,通常使用交叉熵损失函数。损失值越小,表示模型的性能越好。
相关问题
如果要混淆矩阵、召回率、准确度这些呢,代码是什么
当然可以!以下是添加混淆矩阵、召回率、准确度的代码:
1. 导入必要的库和模块
```python
import torch
import torchvision
from torchvision import datasets, models, transforms
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix
import itertools
```
2. 加载数据集
```python
data_dir = 'path/to/dataset'
train_dir = data_dir + '/train'
valid_dir = data_dir + '/valid'
# 定义数据预处理方式
data_transforms = {
'train': transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
'valid': transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
}
# 加载数据集
image_datasets = {
'train': datasets.ImageFolder(train_dir, data_transforms['train']),
'valid': datasets.ImageFolder(valid_dir, data_transforms['valid'])
}
# 定义数据加载器
dataloaders = {
'train': torch.utils.data.DataLoader(image_datasets['train'], batch_size=32, shuffle=True),
'valid': torch.utils.data.DataLoader(image_datasets['valid'], batch_size=32, shuffle=True)
}
```
3. 加载预训练模型
```python
# 使用预训练的 ResNet-18 模型
model = models.resnet18(pretrained=True)
# 将模型的最后一层替换成全连接层
num_ftrs = model.fc.in_features
model.fc = torch.nn.Linear(num_ftrs, 2) # 2 表示分类数量
# 将模型转移到 GPU 上
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = model.to(device)
```
4. 定义损失函数和优化器
```python
criterion = torch.nn.CrossEntropyLoss()
# 只更新最后一层的参数
optimizer = torch.optim.SGD(model.fc.parameters(), lr=0.001, momentum=0.9)
```
5. 训练模型
```python
# 训练模型
num_epochs = 10
for epoch in range(num_epochs):
for phase in ['train', 'valid']:
if phase == 'train':
model.train() # 训练模式
else:
model.eval() # 验证模式
running_loss = 0.0
running_corrects = 0
y_true = []
y_pred = []
for inputs, labels in dataloaders[phase]:
inputs = inputs.to(device)
labels = labels.to(device)
# 将梯度清零
optimizer.zero_grad()
# 前向传播
with torch.set_grad_enabled(phase == 'train'):
outputs = model(inputs)
_, preds = torch.max(outputs, 1)
loss = criterion(outputs, labels)
# 反向传播和优化
if phase == 'train':
loss.backward()
optimizer.step()
# 统计损失和正确预测数
running_loss += loss.item() * inputs.size(0)
running_corrects += torch.sum(preds == labels.data)
y_true += labels.cpu().numpy().tolist()
y_pred += preds.cpu().numpy().tolist()
epoch_loss = running_loss / len(image_datasets[phase])
epoch_acc = running_corrects.double() / len(image_datasets[phase])
epoch_recall = recall_score(y_true, y_pred, average='macro')
epoch_precision = precision_score(y_true, y_pred, average='macro')
epoch_f1 = f1_score(y_true, y_pred, average='macro')
epoch_cm = confusion_matrix(y_true, y_pred)
print('{} Loss: {:.4f} Acc: {:.4f} Recall: {:.4f} Precision: {:.4f} F1: {:.4f}'.format(phase, epoch_loss, epoch_acc, epoch_recall, epoch_precision, epoch_f1))
print('Confusion Matrix:')
print(epoch_cm)
```
以上就是添加混淆矩阵、召回率、准确度的代码,希望能对你有所帮助!
不使用sklearn 采用逻辑回归分类方法对乳腺癌数据进行分类,使用Python编程语言自己实现以下性能评价指标:混淆矩阵、准确率、精确率、召回率、F1值。
好的,我可以给您提供一个基于NumPy实现逻辑回归的代码,并计算混淆矩阵、准确率、精确率、召回率、F1值。
首先,我们需要下载乳腺癌数据集,这里我使用了sklearn中自带的数据集。具体代码如下:
```python
from sklearn.datasets import load_breast_cancer
import numpy as np
# 加载数据集
data = load_breast_cancer()
# 获取特征和标签
X = data.data
y = data.target
# 将标签转换为列向量
y = y.reshape(-1, 1)
# 将数据集按照8:2的比例划分为训练集和测试集
train_size = int(X.shape[0] * 0.8)
X_train, X_test = X[:train_size], X[train_size:]
y_train, y_test = y[:train_size], y[train_size:]
# 特征归一化
mean = X_train.mean(axis=0)
std = X_train.std(axis=0)
X_train = (X_train - mean) / std
X_test = (X_test - mean) / std
```
接下来,我们实现逻辑回归模型。具体代码如下:
```python
class LogisticRegression:
def __init__(self, lr=0.01, num_iter=100000, fit_intercept=True):
self.lr = lr
self.num_iter = num_iter
self.fit_intercept = fit_intercept
def __add_intercept(self, X):
intercept = np.ones((X.shape[0], 1))
return np.concatenate((intercept, X), axis=1)
def __sigmoid(self, z):
return 1 / (1 + np.exp(-z))
def __loss(self, h, y):
return (-y * np.log(h) - (1 - y) * np.log(1 - h)).mean()
def fit(self, X, y):
if self.fit_intercept:
X = self.__add_intercept(X)
# 初始化权重
self.theta = np.zeros((X.shape[1], 1))
for i in range(self.num_iter):
# 计算模型预测值
z = np.dot(X, self.theta)
h = self.__sigmoid(z)
# 计算梯度
gradient = np.dot(X.T, (h - y)) / y.size
# 更新权重
self.theta -= self.lr * gradient
def predict_prob(self, X):
if self.fit_intercept:
X = self.__add_intercept(X)
return self.__sigmoid(np.dot(X, self.theta))
def predict(self, X, threshold=0.5):
return self.predict_prob(X) >= threshold
```
接下来,我们进行模型训练和预测,并计算评价指标。具体代码如下:
```python
# 创建逻辑回归对象
model = LogisticRegression(lr=0.1, num_iter=100000)
# 模型训练
model.fit(X_train, y_train)
# 模型预测
y_pred = model.predict(X_test)
# 混淆矩阵
confusion_matrix = np.zeros((2, 2))
for i in range(len(y_test)):
if y_test[i] == 1 and y_pred[i] == 1:
confusion_matrix[0][0] += 1
elif y_test[i] == 1 and y_pred[i] == 0:
confusion_matrix[0][1] += 1
elif y_test[i] == 0 and y_pred[i] == 1:
confusion_matrix[1][0] += 1
elif y_test[i] == 0 and y_pred[i] == 0:
confusion_matrix[1][1] += 1
# 准确率
accuracy = (confusion_matrix[0][0] + confusion_matrix[1][1]) / np.sum(confusion_matrix)
# 精确率
precision = confusion_matrix[0][0] / (confusion_matrix[0][0] + confusion_matrix[1][0])
# 召回率
recall = confusion_matrix[0][0] / (confusion_matrix[0][0] + confusion_matrix[0][1])
# F1值
f1_score = 2 * precision * recall / (precision + recall)
```
完整的代码如下: