\n\u4f7f\u7528Scikit-learn\u52a0\u8f7d\u6570\u636e\uff1aScikit-learn\u63d0\u4f9b\u4e86\u4e00\u4e9b\u5185\u7f6e\u7684\u6570\u636e\u96c6\uff0c\u53ef\u4ee5\u7528\u4e8e\u6d4b\u8bd5\u548c\u5b9e\u9a8c\u3002<\/p>\n<\/p>\n
from sklearn.datasets import load_iris<\/p>\ndata = load_iris()<\/p>\n
X, y = data.data, data.target<\/p>\n
<\/code><\/pre>\n<\/p>\n<\/li>\n
\n\u4f7f\u7528Pandas\u52a0\u8f7d\u6570\u636e\uff1aPandas\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u636e\u5206\u6790\u5e93\uff0c\u53ef\u4ee5\u7528\u6765\u52a0\u8f7dCSV\u3001Excel\u7b49\u683c\u5f0f\u7684\u6570\u636e\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\ndata = pd.read_csv('data.csv')<\/p>\n
X = data.drop('target', axis=1)<\/p>\n
y = data['target']<\/p>\n
<\/code><\/pre>\n<\/p>\n<\/li>\n
\n\u4f7f\u7528TensorFlow\u52a0\u8f7d\u6570\u636e\uff1aTensorFlow\u63d0\u4f9b\u4e86TFRecord\u683c\u5f0f\u7528\u4e8e\u5b58\u50a8\u548c\u52a0\u8f7d\u5927\u578b\u6570\u636e\u96c6\u3002<\/p>\n<\/p>\n
dataset = tf.data.TFRecordDataset('data.tfrecord')<\/p>\n<\/code><\/pre>\n<\/p>\n<\/li>\n
\n\u4f7f\u7528PyTorch\u52a0\u8f7d\u6570\u636e\uff1aPyTorch\u6709\u81ea\u5df1\u7684\u6570\u636e\u52a0\u8f7d\u6a21\u5757\uff0c\u53ef\u4ee5\u5904\u7406\u56fe\u50cf\u7b49\u6570\u636e\u3002<\/p>\n<\/p>\n
from torchvision import datasets, transforms<\/p>\ntransform = transforms.Compose([transforms.ToTensor()])<\/p>\n
dataset = datasets.MNIST(root='.\/data', train=True, download=True, transform=transform)<\/p>\n
<\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
\u52a0\u8f7d\u6570\u636e\u540e\uff0c\u901a\u5e38\u9700\u8981\u8fdb\u884c\u9884\u5904\u7406\uff0c\u5305\u62ec\u7279\u5f81\u7f29\u653e\u3001\u6570\u636e\u6e05\u6d17\u548c\u62c6\u5206\u8bad\u7ec3\u6d4b\u8bd5\u96c6\u7b49\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u5b9a\u4e49\u548c\u8bad\u7ec3\u6a21\u578b<\/p>\n<\/p>\n
\u5b9a\u4e49\u548c\u8bad\u7ec3\u6a21\u578b\u662f\u673a\u5668\u5b66\u4e60\u7684\u6838\u5fc3\u6b65\u9aa4\u3002\u5728\u6b64\u8fc7\u7a0b\u4e2d\uff0c\u9700\u8981\u9009\u62e9\u5408\u9002\u7684\u6a21\u578b\u67b6\u6784\u548c\u8bad\u7ec3\u7b97\u6cd5\u3002<\/p>\n<\/p>\n
\n- \n
\u4f7f\u7528Scikit-learn\u5b9a\u4e49\u548c\u8bad\u7ec3\u6a21\u578b\uff1aScikit-learn\u63d0\u4f9b\u4e86\u7b80\u5355\u7684\u63a5\u53e3\u6765\u5b9a\u4e49\u548c\u8bad\u7ec3\u6a21\u578b\u3002<\/p>\n<\/p>\n
model = RandomForestClassifier(n_estimators=100)<\/p>\nmodel.fit(X_train, y_train)<\/p>\n
<\/code><\/pre>\n<\/p>\n<\/li>\n
- \n
\u4f7f\u7528TensorFlow\/Keras\u5b9a\u4e49\u548c\u8bad\u7ec3\u6a21\u578b\uff1aTensorFlow\/Keras\u652f\u6301\u5b9a\u4e49\u590d\u6742\u7684\u795e\u7ecf\u7f51\u7edc\u6a21\u578b\u3002<\/p>\n<\/p>\n
model = keras.Sequential([<\/p>\n keras.layers.Dense(128, activation='relu', input_shape=(input_shape,)),<\/p>\n
keras.layers.Dense(10, activation='softmax')<\/p>\n
])<\/p>\n
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])<\/p>\n
model.fit(X_train, y_train, epochs=10, batch_size=32)<\/p>\n
<\/code><\/pre>\n<\/p>\n<\/li>\n
- \n
\u4f7f\u7528PyTorch\u5b9a\u4e49\u548c\u8bad\u7ec3\u6a21\u578b\uff1aPyTorch\u7684\u52a8\u6001\u8ba1\u7b97\u56fe\u4f7f\u6a21\u578b\u5b9a\u4e49\u66f4\u52a0\u7075\u6d3b\u3002<\/p>\n<\/p>\n
class Net(nn.Module):<\/p>\n def __init__(self):<\/p>\n
super(Net, self).__init__()<\/p>\n
self.fc1 = nn.Linear(input_size, 128)<\/p>\n
self.fc2 = nn.Linear(128, num_classes)<\/p>\n
def forward(self, x):<\/p>\n
x = torch.relu(self.fc1(x))<\/p>\n
x = self.fc2(x)<\/p>\n
return x<\/p>\n
model = Net()<\/p>\n
criterion = nn.CrossEntropyLoss()<\/p>\n
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)<\/p>\n
for epoch in range(10):<\/p>\n
for data, target in train_loader:<\/p>\n
optimizer.zero_grad()<\/p>\n
output = model(data)<\/p>\n
loss = criterion(output, target)<\/p>\n
loss.backward()<\/p>\n
optimizer.step()<\/p>\n
<\/code><\/pre>\n<\/p>\n<\/li>\n
- \n
\u4f7f\u7528XGBoost\u5b9a\u4e49\u548c\u8bad\u7ec3\u6a21\u578b\uff1aXGBoost\u7684\u63a5\u53e3\u7c7b\u4f3c\u4e8eScikit-learn\u3002<\/p>\n<\/p>\n
model = xgb.XGBClassifier(n_estimators=100)<\/p>\nmodel.fit(X_train, y_train)<\/p>\n
<\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\uff0c\u53ef\u80fd\u9700\u8981\u8fdb\u884c\u8d85\u53c2\u6570\u8c03\u4f18\uff0c\u4ee5\u627e\u5230\u6700\u4f73\u7684\u6a21\u578b\u53c2\u6570\u3002<\/p>\n<\/p>\n
\u516d\u3001\u8bc4\u4f30\u6a21\u578b\u6027\u80fd<\/p>\n<\/p>\n
\u8bc4\u4f30\u6a21\u578b\u6027\u80fd\u662f\u673a\u5668\u5b66\u4e60\u6d41\u7a0b\u4e2d\u7684\u91cd\u8981\u73af\u8282\uff0c\u7528\u4e8e\u9a8c\u8bc1\u6a21\u578b\u7684\u6709\u6548\u6027\u548c\u51c6\u786e\u6027\u3002<\/p>\n<\/p>\n
\n- \n
\u4f7f\u7528Scikit-learn\u8bc4\u4f30\u6a21\u578b\u6027\u80fd\uff1aScikit-learn\u63d0\u4f9b\u4e86\u591a\u79cd\u8bc4\u4f30\u6307\u6807\uff0c\u5982\u51c6\u786e\u7387\u3001\u7cbe\u786e\u7387\u3001\u53ec\u56de\u7387\u7b49\u3002<\/p>\n<\/p>\n
from sklearn.metrics import accuracy_score, classification_report<\/p>\ny_pred = model.predict(X_test)<\/p>\n
accuracy = accuracy_score(y_test, y_pred)<\/p>\n
report = classification_report(y_test, y_pred)<\/p>\n
<\/code><\/pre>\n<\/p>\n<\/li>\n
- \n
\u4f7f\u7528TensorFlow\/Keras\u8bc4\u4f30\u6a21\u578b\u6027\u80fd\uff1aKeras\u63d0\u4f9b\u4e86evaluate\u65b9\u6cd5\uff0c\u7528\u4e8e\u8bc4\u4f30\u6a21\u578b\u5728\u6d4b\u8bd5\u96c6\u4e0a\u7684\u8868\u73b0\u3002<\/p>\n<\/p>\n
test_loss, test_acc = model.evaluate(X_test, y_test)<\/p>\n<\/code><\/pre>\n<\/p>\n<\/li>\n
- \n
\u4f7f\u7528PyTorch\u8bc4\u4f30\u6a21\u578b\u6027\u80fd\uff1aPyTorch\u9700\u8981\u624b\u52a8\u8ba1\u7b97\u8bc4\u4f30\u6307\u6807\u3002<\/p>\n<\/p>\n
correct = 0<\/p>\ntotal = 0<\/p>\n
with torch.no_grad():<\/p>\n
for data, target in test_loader:<\/p>\n
outputs = model(data)<\/p>\n
_, predicted = torch.max(outputs.data, 1)<\/p>\n
total += target.size(0)<\/p>\n
correct += (predicted == target).sum().item()<\/p>\n
accuracy = correct \/ total<\/p>\n
<\/code><\/pre>\n<\/p>\n<\/li>\n
- \n
\u4f7f\u7528XGBoost\u8bc4\u4f30\u6a21\u578b\u6027\u80fd\uff1aXGBoost\u4e5f\u53ef\u4ee5\u4f7f\u7528Scikit-learn\u7684\u8bc4\u4f30\u6307\u6807\u3002<\/p>\n<\/p>\n
y_pred = model.predict(X_test)<\/p>\naccuracy = accuracy_score(y_test, y_pred)<\/p>\n
<\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
\u8bc4\u4f30\u6a21\u578b\u6027\u80fd\u65f6\uff0c\u5e94\u6839\u636e\u95ee\u9898\u7684\u5177\u4f53\u6027\u8d28\u9009\u62e9\u5408\u9002\u7684\u8bc4\u4f30\u6307\u6807\u3002<\/p>\n<\/p>\n
\u4e03\u3001\u8d85\u53c2\u6570\u8c03\u4f18<\/p>\n<\/p>\n
\u8d85\u53c2\u6570\u8c03\u4f18\u662f\u63d0\u9ad8\u6a21\u578b\u6027\u80fd\u7684\u5173\u952e\u6b65\u9aa4\uff0c\u901a\u8fc7\u8c03\u6574\u6a21\u578b\u7684\u8d85\u53c2\u6570\u6765\u4f18\u5316\u5176\u8868\u73b0\u3002<\/p>\n<\/p>\n
\n- \n
\u4f7f\u7528Scikit-learn\u8fdb\u884c\u8d85\u53c2\u6570\u8c03\u4f18\uff1aScikit-learn\u63d0\u4f9b\u4e86GridSearchCV\u548cRandomizedSearchCV\u7b49\u5de5\u5177\u3002<\/p>\n<\/p>\n
from sklearn.model_selection import GridSearchCV<\/p>\nparam_grid = {'n_estimators': [50, 100, 150]}<\/p>\n
grid_search = GridSearchCV(RandomForestClassifier(), param_grid, cv=5)<\/p>\n
grid_search.fit(X_train, y_train)<\/p>\n
best_params = grid_search.best_params_<\/p>\n
<\/code><\/pre>\n<\/p>\n<\/li>\n
- \n
\u4f7f\u7528TensorFlow\/Keras\u8fdb\u884c\u8d85\u53c2\u6570\u8c03\u4f18\uff1a\u53ef\u4ee5\u4f7f\u7528Keras Tuner\u8fdb\u884c\u8d85\u53c2\u6570\u641c\u7d22\u3002<\/p>\n<\/p>\n
from kerastuner.tuners import RandomSearch<\/p>\ntuner = RandomSearch(<\/p>\n
build_model,<\/p>\n
objective='val_accuracy',<\/p>\n
max_trials=5,<\/p>\n
executions_per_trial=3,<\/p>\n
directory='my_dir',<\/p>\n
project_name='helloworld')<\/p>\n
tuner.search(X_train, y_train, epochs=10, validation_data=(X_val, y_val))<\/p>\n
best_hps = tuner.get_best_hyperparameters(num_trials=1)[0]<\/p>\n
<\/code><\/pre>\n<\/p>\n<\/li>\n
- \n
\u4f7f\u7528PyTorch\u8fdb\u884c\u8d85\u53c2\u6570\u8c03\u4f18\uff1a\u901a\u5e38\u9700\u8981\u624b\u52a8\u5b9e\u73b0\u8d85\u53c2\u6570\u641c\u7d22\u8fc7\u7a0b\u3002<\/p>\n<\/p>\n
# Example code for manual hyperparameter search<\/p>\nfor lr in [0.001, 0.01, 0.1]:<\/p>\n
optimizer = torch.optim.Adam(model.parameters(), lr=lr)<\/p>\n
# Train and evaluate model<\/p>\n
<\/code><\/pre>\n<\/p>\n<\/li>\n
- \n
\u4f7f\u7528XGBoost\u8fdb\u884c\u8d85\u53c2\u6570\u8c03\u4f18\uff1a\u53ef\u4ee5\u7ed3\u5408Scikit-learn\u7684\u5de5\u5177\u8fdb\u884c\u8c03\u4f18\u3002<\/p>\n<\/p>\n
from sklearn.model_selection import RandomizedSearchCV<\/p>\nparam_dist = {'n_estimators': [50, 100, 150], 'learning_rate': [0.01, 0.1, 0.2]}<\/p>\n
random_search = RandomizedSearchCV(xgb.XGBClassifier(), param_distributions=param_dist, n_iter=10, cv=5)<\/p>\n
random_search.fit(X_train, y_train)<\/p>\n
best_params = random_search.best_params_<\/p>\n
<\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
\u8d85\u53c2\u6570\u8c03\u4f18\u53ef\u4ee5\u663e\u8457\u63d0\u9ad8\u6a21\u578b\u7684\u6027\u80fd\uff0c\u4f46\u9700\u8981\u8017\u8d39\u8f83\u591a\u7684\u8ba1\u7b97\u8d44\u6e90\u3002<\/p>\n<\/p>\n
\u516b\u3001\u4fdd\u5b58\u548c\u52a0\u8f7d\u6a21\u578b<\/p>\n<\/p>\n
\u5728\u8bad\u7ec3\u597d\u6a21\u578b\u540e\uff0c\u4fdd\u5b58\u6a21\u578b\u4ee5\u4fbf\u540e\u7eed\u4f7f\u7528\u662f\u975e\u5e38\u91cd\u8981\u7684\u4e00\u6b65\u3002<\/p>\n<\/p>\n
\n- \n
\u4f7f\u7528Scikit-learn\u4fdd\u5b58\u548c\u52a0\u8f7d\u6a21\u578b\uff1aScikit-learn\u53ef\u4ee5\u4f7f\u7528joblib\u6216pickle\u6765\u4fdd\u5b58\u548c\u52a0\u8f7d\u6a21\u578b\u3002<\/p>\n<\/p>\n
import joblib<\/p>\njoblib.dump(model, 'model.joblib')<\/p>\n
loaded_model = joblib.load('model.joblib')<\/p>\n
<\/code><\/pre>\n<\/p>\n<\/li>\n
- \n
\u4f7f\u7528TensorFlow\/Keras\u4fdd\u5b58\u548c\u52a0\u8f7d\u6a21\u578b\uff1aKeras\u63d0\u4f9b\u4e86\u591a\u79cd\u4fdd\u5b58\u6a21\u578b\u7684\u65b9\u5f0f\uff0c\u5305\u62ec\u4fdd\u5b58\u6574\u4e2a\u6a21\u578b\u548c\u4ec5\u4fdd\u5b58\u6743\u91cd\u3002<\/p>\n<\/p>\n
model.save('model.h5')<\/p>\nloaded_model = keras.models.load_model('model.h5')<\/p>\n
<\/code><\/pre>\n<\/p>\n<\/li>\n
- \n
\u4f7f\u7528PyTorch\u4fdd\u5b58\u548c\u52a0\u8f7d\u6a21\u578b\uff1aPyTorch\u53ef\u4ee5\u901a\u8fc7\u4fdd\u5b58\u548c\u52a0\u8f7d\u6a21\u578b\u7684\u72b6\u6001\u5b57\u5178\u6765\u5b9e\u73b0\u3002<\/p>\n<\/p>\n
torch.save(model.state_dict(), 'model.pth')<\/p>\nmodel.load_state_dict(torch.load('model.pth'))<\/p>\n
<\/code><\/pre>\n<\/p>\n<\/li>\n
- \n
\u4f7f\u7528XGBoost\u4fdd\u5b58\u548c\u52a0\u8f7d\u6a21\u578b\uff1aXGBoost\u63d0\u4f9b\u4e86\u4fdd\u5b58\u548c\u52a0\u8f7d\u6a21\u578b\u7684\u63a5\u53e3\u3002<\/p>\n<\/p>\n
model.save_model('model.json')<\/p>\nloaded_model = xgb.XGBClassifier()<\/p>\n
loaded_model.load_model('model.json')<\/p>\n
<\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
\u4fdd\u5b58\u548c\u52a0\u8f7d\u6a21\u578b\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u6a21\u578b\u7684\u5206\u53d1\u3001\u90e8\u7f72\u548c\u5206\u4eab\u3002<\/p>\n<\/p>\n
\u901a\u8fc7\u4ee5\u4e0a\u6b65\u9aa4\uff0c\u53ef\u4ee5\u6709\u6548\u5730\u4f7f\u7528Python\u7684\u6a21\u578b\u5305\u6765\u8fdb\u884c\u673a\u5668\u5b66\u4e60\u548c\u6df1\u5ea6\u5b66\u4e60\u4efb\u52a1\u3002\u6bcf\u4e2a\u6b65\u9aa4\u90fd\u9700\u8981\u6839\u636e\u5177\u4f53\u7684\u9879\u76ee\u9700\u6c42\u8fdb\u884c\u8c03\u6574\uff0c\u4ee5\u786e\u4fdd\u5f97\u5230\u6700\u4f73\u7684\u6a21\u578b\u6027\u80fd\u548c\u6548\u679c\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n \u5982\u4f55\u9009\u62e9\u5408\u9002\u7684Python\u6a21\u578b\u5305\u8fdb\u884c\u9879\u76ee\u5f00\u53d1\uff1f<\/strong>
\u5728\u9009\u62e9Python\u6a21\u578b\u5305\u65f6\uff0c\u9700\u8981\u8003\u8651\u591a\u4e2a\u56e0\u7d20\uff0c\u5305\u62ec\u6a21\u578b\u7684\u9002\u7528\u6027\u3001\u793e\u533a\u652f\u6301\u3001\u6587\u6863\u7684\u5b8c\u6574\u6027\u4ee5\u53ca\u66f4\u65b0\u9891\u7387\u3002\u5e38\u7528\u7684\u6a21\u578b\u5305\u5982Scikit-learn\u3001TensorFlow\u548cPyTorch\uff0c\u5404\u6709\u5176\u4f18\u7f3a\u70b9\u3002\u5efa\u8bae\u5148\u660e\u786e\u9879\u76ee\u9700\u6c42\uff0c\u6bd4\u5982\u662f\u8fdb\u884c\u4f20\u7edf\u7684\u673a\u5668\u5b66\u4e60\u4efb\u52a1\u8fd8\u662f\u6df1\u5ea6\u5b66\u4e60\uff0c\u968f\u540e\u67e5\u770b\u5404\u5305\u7684\u529f\u80fd\u548c\u793a\u4f8b\u4ee3\u7801\uff0c\u4ee5\u4fbf\u505a\u51fa\u660e\u667a\u7684\u9009\u62e9\u3002<\/p>\nPython\u6a21\u578b\u5305\u7684\u5b89\u88c5\u8fc7\u7a0b\u662f\u600e\u6837\u7684\uff1f<\/strong>
\u5b89\u88c5Python\u6a21\u578b\u5305\u901a\u5e38\u53ef\u4ee5\u901a\u8fc7Python\u5305\u7ba1\u7406\u5de5\u5177pip\u6765\u5b8c\u6210\u3002\u53ea\u9700\u5728\u547d\u4ee4\u884c\u4e2d\u8f93\u5165pip install \u5305\u540d<\/code>\uff0c\u4f8b\u5982pip install scikit-learn<\/code>\uff0c\u5373\u53ef\u5b89\u88c5\u6240\u9700\u7684\u5305\u3002\u786e\u4fdd\u4f7f\u7528\u7684Python\u7248\u672c\u4e0e\u5305\u517c\u5bb9\uff0c\u6b64\u5916\uff0c\u865a\u62df\u73af\u5883\uff08\u5982venv\u6216conda\uff09\u53ef\u4ee5\u5e2e\u52a9\u7ba1\u7406\u4e0d\u540c\u9879\u76ee\u6240\u9700\u7684\u4f9d\u8d56\u3002<\/p>\n\u5728\u4f7f\u7528Python\u6a21\u578b\u5305\u65f6\uff0c\u5982\u4f55\u5904\u7406\u6570\u636e\u9884\u5904\u7406\u95ee\u9898\uff1f<\/strong>
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