scikit-learn | 易学教程

Perform feature selection using pipeline and gridsearch

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问题 As part of a research project, I want to select the best combination of preprocessing techniques and textual features that optimize the results of a text classification task. For this, I am using Python 3.6. There are a number of methods to combine features and algorithms, but I want to take full advantage of sklearn's pipelines and test all the different (valid) possibilities using grid search for the ultimate feature combo. My first step was to build a pipeline that looks like the following

Perform feature selection using pipeline and gridsearch

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sklearn pipeline + keras sequential model - how to get history?

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问题 Keras models, when .fit is called, return a history object. Is it possible to retrieve it if I use this model as one step of a sklearn pipeline? btw, i'm using python 3.6 Thanks in advance! 回答1: The History callback records training metrics for each epoch. This includes the loss and the accuracy (for classification problems) as well as the loss and accuracy for the validation dataset, if one is set. The history object is returned from calls to the fit() function used to train the model.

how to resolve this ValueError: only 2 non-keyword arguments accepted sklearn python

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问题 hello i am new to sklearn in python and iam trying to learn it and use this module to predict some numbers based on two features here is the error i am getting: ValueError: only 2 non-keyword arguments accepted and here is my code: from sklearn.linear_model import LinearRegression import numpy as np trainingData = np.array([[861, 16012018], [860, 12012018], [859, 9012018], [858, 5012018], [857, 2012018], [856, 29122017], [855, 26122017], [854, 22122017], [853, 19122017]]) trainingScores = np

how to resolve this ValueError: only 2 non-keyword arguments accepted sklearn python

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How to get the topic probability for each document for topic modeling using LDA

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问题 I use scikit-learn LDA to generate LDA model and after that I can get the topic-terms. I am wondering how can I get the probability of each topic for each document? 回答1: Use the transform method of the LatentDirichletAllocation class after fitting the model. It will return the document topic distribution. If you work with the example given in the documentation for scikit-learn's Latent Dirichlet Allocation, the document topic distribution can be accessed by appending the following line to the

ValueError: non-string names in Numpy dtype unpickling only on AWS Lambda

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问题 I am using pickle to save my trained ML model. For the learning part, I am using scikit-learn library and building a RandomForestClassifier rf = RandomForestClassifier(n_estimators=100, max_depth=20, min_samples_split=2, max_features='auto', oob_score=True, random_state=123456) rf.fit(X, y) fp = open('model.pckl', 'wb') pickle.dump(rf, fp, protocol=2) fp.close() I uploaded this model on S3 and I am fetching this model using boto3 library in AWS Lambda. s3_client = boto3.client('s3') bucket =

ValueError: non-string names in Numpy dtype unpickling only on AWS Lambda

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机器学习 | 一个基于机器学习的简单小实践：波斯顿房价预测分析

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本文采用Kaggle上面的Boston HousePrice数据集展示了如何建立机器学习模型的通常过程，包括以下几个阶段：数据获取数据清洗探索性数据分析特征工程模型建立模型集成标签变量（房价）采取了对数转换，使其符合正太分布，最后从12个备选模型中选出预测效果最好的6个模型Lasso，Ridge，SVR，KernelRidge，ElasticNet，BayesianRidge分别进行加权平均集成和Stacking集成，最后发现Stacking集成效果更好，创新之处在于将Stacking集成后的数据加入原训练集中再次训练Stacking集成模型，使得模型性能再次得到改善，作为最后的预测模型，预测结果提交kaggle上后表现不错。另外受限于训练时间，超参数搜索空间小，有待改善。数据获取 Kaggle官网提供了大量的机器学习数据集，本文从其中选择了Boston HousePrice数据集，下载地址为https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data，下载后的数据集包括train.csv，test.csv，data_description.txt，sample_submission.csv四个文件，顾名思义train.csv为训练数据集，用于训练模型，test

深度学习“四大名著”发布！Python、TensorFlow、机器学习、深度学习四件套（附免费下载）

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Python 程序员深度学习的“四大名著”：这四本书着实很不错！我们都知道现在机器学习、深度学习的资料太多了，面对海量资源，往往陷入到“无从下手”的困惑出境。而且并非所有的书籍都是优质资源，浪费大量的时间是得不偿失的。给大家推荐这几本好书并做简单介绍。获得方式： 1.扫码关注 “涛哥聊python” 公众号 2.后台回复关键词： 4books 注：此处建议复制，不然容易打错 ▲长按扫描关注，回复 4books 即可获取 1. 《Deep Learning with Python》推荐指数：★★★★☆ 本书自出版以来收到众多好评，因为是 Keras 作者写的书，所以全书基本围绕着 Keras 讲深度学习的各种实现，从 CNN，RNN 到 GAN 等，偏入门，但也承载着很多作者对深度学习整体性的思考。这是一本偏实战的书，教你使用 Keras 快速实现深度学习经典项目。看完这本书，基本能对 Keras 和深度学习实战有比较初步的掌握了。本书源码 GitHub 地址： https://github.com/fchollet/deep-learning-with-python-notebooks 2. 《Python Machine Learning》推荐指数：★★★☆☆ 本书使用了 Scikit-Learn 和 TensorFlow，分别讲解机器学习和深度学习

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