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当新数据到来时如何在pyspark中重新训练保存的线性回归ML模型

[英]how to re-train Saved linear regression ML model in pyspark when new data is coming

原文 2017-07-12 07:15:01 8 1 python/ machine-learning/ pyspark

我使用pyspark ml训练了线性回归模型并保存了它。现在我想根据新的数据批重新训练它。

1 个解决方案

I don't think so, you use pyspark.ml.regression.GeneralizedLinearRegression to train, and then you get a pyspark.ml.regression.GeneralizedLinearRegressionModel, that is what you have saved. 我不这么认为，您可以使用pyspark.ml.regression.GeneralizedLinearRegression进行训练，然后获得一个pyspark.ml.regression.GeneralizedLinearRegressionModel，这就是您保存的内容。 AFIK, the model can't be refitted, you have to use the regression fit again to get a new model. AFIK，无法重新拟合模型，您必须再次使用回归拟合才能获得新模型。

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相关问题 如何为货币重新训练现有的Spacy NER模型 - How to re-train an existing spacy NER model for currency 如何为线性回归和训练模型创建 tf.data.Datasets - How to create a tf.data.Datasets for linear regression and train model 如何调整/重新训练 NLTK SentimentIntensityAnalyzer - How to adjust/re-train NLTK SentimentIntensityAnalyzer 如何重新训练现有的K-Means聚类模型 - How do I re-train an existing K-Means clustering model 是否需要重新训练 BERT 模型，特别是 RoBERTa 模型？ - Is it necessary to re-train BERT models, specifically RoBERTa model? 如何传递单个列因变量来训练线性回归模型？ - How to pass the single columnar dependent variable to train the linear regression model? Tensorflow：如何训练线性回归？ - Tensorflow: How to train a Linear Regression? 在 Keras 中，训练有状态 LSTM model 后，是否必须在预测值时重新训练 model？ - In Keras, after you train a stateful LSTM model, do you have to re-train the model as you predict values? 重新训练预先训练好的ResNet-50型号，使用tf slim进行分类 - Re-train pre-trained ResNet-50 model with tf slim for classification purposes 使用相同的数据集重新训练保存的模型 - Re train a saved model with same Dataset

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