pysparkML预测
时间: 2023-10-05 20:14:09 浏览: 64
要使用pysparkML进行预测,首先需要加载数据集并进行数据预处理。根据引用[1]和引用的代码,我们可以使用VectorAssembler将特征列合并为一个向量,并将其作为输入特征,然后使用相应的分类算法进行模型训练和预测。
以下是一个示例代码框架:
```
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.classification import LogisticRegression
# 加载数据集
trainSet = spark.read.csv(r'/Users/sherry/Downloads/iris_training.csv', schema='Feat1 FLOAT,Feat2 FLOAT,Feat3 FLOAT,Feat4 FLOAT,label INT')
testSet = spark.read.csv(r'/Users/sherry/Downloads/iris_test.csv', schema='Feat1 FLOAT,Feat2 FLOAT,Feat3 FLOAT,Feat4 FLOAT,label INT')
# 合并特征列为向量
vect_assemble = VectorAssembler(inputCols=['Feat1','Feat2','Feat3','Feat4'], outputCol='features')
trainSet = vect_assemble.transform(trainSet)
testSet = vect_assemble.transform(testSet)
# 创建并训练逻辑回归模型
lr = LogisticRegression(featuresCol='features', labelCol='label')
lr_model = lr.fit(trainSet)
# 进行预测
predictions = lr_model.transform(testSet)
predictions.select('features', 'prediction').show(5)
```
请注意,上述代码仅是一个示例,实际使用时需要根据具体的数据集和需求进行相应的调整和修改。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
- *1* *2* [Pyspark:ml包中Classification\Clustering\Regression包的使用](https://blog.csdn.net/yeshang_lady/article/details/127375014)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_1"}}] [.reference_item style="max-width: 50%"]
- *3* [logisticCurvesKeras](https://download.csdn.net/download/weixin_42120997/15162896)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_1"}}] [.reference_item style="max-width: 50%"]
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