The impact of data set sampling strategy on model performance

The impact of data set sampling strategy on model performance requires specific code examples

With the rapid development of machine learning and deep learning, the quality and scale of the data set The impact on model performance is becoming increasingly important. In practical applications, we often face problems such as excessive data set size, unbalanced sample categories, and sample noise. At this time, a reasonable choice of sampling strategy can improve the performance and generalization ability of the model. This article will discuss the impact of different data set sampling strategies on model performance through specific code examples.

1. Random Sampling
   Random sampling is one of the most common data set sampling strategies. During the training process, we randomly select a certain proportion of samples from the data set as the training set. This method is simple and intuitive, but it may lead to an unbalanced distribution of sample categories or the loss of important samples. Here is a sample code:

import numpy as np

def random_sampling(X, y, sample_ratio):
    num_samples = int(sample_ratio * X.shape[0])
    indices = np.random.choice(X.shape[0], num_samples, replace=False)
    X_sampled = X[indices]
    y_sampled = y[indices]
    return X_sampled, y_sampled

2. stratified sampling
   Stratified sampling is a common strategy to solve the problem of sample class imbalance. In stratified sampling, we stratify the data set according to the categories of samples and select a proportion of samples from each category. This method can maintain the proportion of each category in the data set, thereby improving the model's ability to handle minority categories. The following is a sample code:

from sklearn.model_selection import train_test_split
from sklearn.utils import resample

def stratified_sampling(X, y, sample_ratio):
    X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, test_size=1-sample_ratio)
    X_sampled, y_sampled = resample(X_train, y_train, n_samples=int(sample_ratio * X.shape[0]))
    return X_sampled, y_sampled

3. Edge Sampling
   Edge sampling is a common strategy to solve the problem of sample noise. In edge sampling, we divide samples into reliable samples and noise samples by learning a model, and then only select reliable samples for training. The following is a sample code:

from sklearn.svm import OneClassSVM

def margin_sampling(X, y, sample_ratio):
    clf = OneClassSVM(gamma='scale')
    clf.fit(X)
    y_pred = clf.predict(X)
    reliable_samples = X[y_pred == 1]
    num_samples = int(sample_ratio * X.shape[0])
    indices = np.random.choice(reliable_samples.shape[0], num_samples, replace=False)
    X_sampled = reliable_samples[indices]
    y_sampled = y[indices]
    return X_sampled, y_sampled

In summary, different data set sampling strategies have different impacts on model performance. Random sampling can easily and quickly obtain the training set, but it may lead to unbalanced sample categories; stratified sampling can maintain the balance of sample categories and improve the model's ability to handle minority categories; edge sampling can filter out noisy samples and improve the robustness of the model sex. In practical applications, we need to choose an appropriate sampling strategy based on specific problems, and select the optimal strategy through experiments and evaluations to improve the performance and generalization ability of the model.

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