PHP-ML机器学习库：问答内容提取示例与教程

PHP-ML 是一个用于机器学习的 PHP 库。它提供了一组简单易用的机器学习算法和工具，可以用于数据预处理、特征提取、模型训练和预测等任务。

以下是一个使用 PHP-ML 进行问答内容提取的简单示例：

require 'vendor/autoload.php';

use Phpml\Tokenization\WhitespaceTokenizer;
use Phpml\FeatureExtraction\TfIdfTransformer;
use Phpml\FeatureExtraction\TokenCountVectorizer;
use Phpml\FeatureExtraction\StopWords\English;
use Phpml\Tokenization\WordTokenizer;
use Phpml\CrossValidation\StratifiedRandomSplit;
use Phpml\Classification\SVC;
use Phpml\FeatureExtraction\StopWords\Chinese;
use Phpml\Pipeline;
use Phpml\FeatureExtraction\TfIdfTransformer as TfIdf;
use Phpml\Tokenization\NGramTokenizer;
use Phpml\Metric\Accuracy;
use Phpml\Dataset\CsvDataset;
use Phpml\Dataset\ArrayDataset;
use Phpml\Classification\KNearestNeighbors;
use Phpml\FeatureExtraction\StopWords\Turkish;
use Phpml\FeatureExtraction\StopWords\Vietnamese;
use Phpml\Classification\NaiveBayes;
use Phpml\Classification\RandomForest;
use Phpml\Regression\SVR;
use Phpml\Classification\GaussianNB;
use Phpml\Regression\LeastSquares;
use Phpml\FeatureExtraction\StopWords\French;
use Phpml\FeatureExtraction\TokenCountVectorizer as CountVectorizer;
use Phpml\Regression\DecisionTreeRegressor;
use Phpml\Metric\ClassificationReport;
use Phpml\Regression\KNearestNeighbors as KNN;
use Phpml\Regression\RandomForest as RF;
use Phpml\Regression\SVR as SVM;
use Phpml\Regression\LeastSquares as LS;
use Phpml\Regression\DecisionTreeRegressor as DT;
use Phpml\Metric\ConfusionMatrix;
use Phpml\Association\Apriori;
use Phpml\Clustering\KMeans;

// 加载训练和测试数据集
$dataset = new CsvDataset('dataset.csv', 1, true);

// 分割数据集为训练集和测试集
$split = new StratifiedRandomSplit($dataset, 0.3);

// 使用管道定义数据处理和分类器
$pipeline = new Pipeline([
    new TokenCountVectorizer(new WordTokenizer()),
    new TfIdfTransformer(),
], new NaiveBayes());

// 训练分类器
$pipeline->train($split->getTrainSamples(), $split->getTrainLabels());

// 预测测试集
$predictedLabels = $pipeline->predict($split->getTestSamples());

// 计算准确率
$accuracy = Accuracy::score($split->getTestLabels(), $predictedLabels);

// 输出结果
echo '准确率: ' . $accuracy;

以上示例代码使用了朴素贝叶斯算法进行问答内容提取的分类任务，并计算了准确率。你可以将训练和测试数据集替换为你自己的数据集，调整算法选择和参数以满足你的需求。