# Split the dataset into training and testing sets from sklearn.model_selection import train_test_split
def split_dataset(data, test_size=0.2): # Separate features (X) and target variable (y) X = data.drop('target_variable', axis=1) y = data['target_variable']
# Split the dataset X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size)
return X_train, X_test, y_train, y_test
问题: 使用 scikit-learn 编写一个 Python 脚本来执行 k-means 聚类。
答案:
# Perform k-means clustering from sklearn.cluster import KMeans
# Load the data data = ...
# Create and fit the k-means model with a specified number of clusters (e.g., 4) model = KMeans(n_clusters=4) model.fit(data)
# Predict cluster labels for each data point cluster_labels = model.predict(data)
问题: 编写一个 Python 函数来查找两个变量之间的相关性。
答案:
# Calculate the correlation between two variables from scipy.stats import pearsonr
# Perform principal component analysis (PCA) from sklearn.decomposition import PCA
# Load the data data = ...
# Create and fit the PCA model with a specified number of components (e.g., 2) model = PCA(n_components=2) transformed_data = model.fit_transform(data)
问题: 编写一个 Python 函数,对数据集进行规范化处理。
答案:
# Normalize the dataset from sklearn.preprocessing import StandardScaler
def normalize_dataset(data): # Use StandardScaler to normalize the data scaler = StandardScaler() normalized_data = scaler.fit_transform(data)
return normalized_data
问题: 编写一个 Python 脚本,使用 t-SNE 进行降维。
答案:
from sklearn.manifold import TSNE
# Load the data data = ...
# Create and fit the t-SNE model model = TSNE(n_components=2) reduced_data = model.fit_transform(data)
问题: 编写一个 Python 函数,为机器学习模型实现自定义损失函数。
答案:
import tensorflow as tf
def custom_loss_function(y_true, y_pred): loss = tf.keras.losses.categorical_crossentropy(y_true, y_pred) return loss
问题: 使用 TensorFlow 编写 Python 脚本,训练自定义神经网络模型。
答案:
import tensorflow as tf
# Define the model architecture model = tf.keras.Sequential([ tf.keras.layers.Dense(64, activation='relu', input_shape=(data.shape[1],)), tf.keras.layers.Dense(32, activation='relu'), tf.keras.layers.Dense(10, activation='softmax') ])
# Compile the model model.compile(loss='custom_loss_function', optimizer='adam', metrics=['accuracy'])
# Train the model model.fit(X_train, y_train, epochs=10, batch_size=32)