knn，决策树，逻辑回归，线性回归，支持向量机，卷积神经网络算法之间的区别用Python代码表示

KNN算法:

from sklearn.neighbors import KNeighborsClassifier

# 创建KNN分类器对象
knn = KNeighborsClassifier(n_neighbors=3)

# 训练模型
knn.fit(X_train, y_train)

# 预测结果
y_pred = knn.predict(X_test)

决策树算法:

from sklearn.tree import DecisionTreeClassifier

# 创建决策树分类器对象
dt = DecisionTreeClassifier(criterion='entropy')

# 训练模型
dt.fit(X_train, y_train)

# 预测结果
y_pred = dt.predict(X_test)

逻辑回归算法:

from sklearn.linear_model import LogisticRegression

# 创建逻辑回归模型对象
lr = LogisticRegression()

# 训练模型
lr.fit(X_train, y_train)

# 预测结果
y_pred = lr.predict(X_test)

线性回归算法:

from sklearn.linear_model import LinearRegression

# 创建线性回归模型对象
lr = LinearRegression()

# 训练模型
lr.fit(X_train, y_train)

# 预测结果
y_pred = lr.predict(X_test)

支持向量机算法:

from sklearn.svm import SVC

# 创建SVM分类器对象
svc = SVC(kernel='linear')

# 训练模型
svc.fit(X_train, y_train)

# 预测结果
y_pred = svc.predict(X_test)

卷积神经网络算法:

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv2D, MaxPooling2D, Flatten

# 创建卷积神经网络模型
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
model.add(MaxPooling2D((2, 2)))
model.add(Flatten())
model.add(Dense(10, activation='softmax'))

# 编译模型
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# 训练模型
model.fit(X_train, y_train, epochs=10, validation_data=(X_test, y_test))

# 预测结果
y_pred = model.predict(X_test)