线性回归(Linear regression)是利用回归方程(函数)对一个或多个自变量(特征值)和因变量(目标值)之间关系进行建模的一种分析方式。 通用公式: 应用场景: 1.房价预测 2.销售额度预测 3.贷款额度预测 一.案例背景介绍 # -*- coding: utf-8 -*- # @Time : 2019/11/12 11:46 # @Author : from sklearn....
此数据集由Bart de Cock于2011年收集 (链接:Ames, Iowa: Alternative to the Boston Housing Data as an End of Semester Regression Project: Journal of Statistics Education: Vol 19, No 3 (tandfonline.com)), 涵盖了2006-2010年期间亚利桑那州埃姆斯市的房价。 这个数据集是相当通用的,不会需要使用复杂模...
上周一个叫 Abhishek Thakur 的数据科学家,在他的 Linkedin 发表了一篇文章 Approaching (Almost) Any Machine Learning Problem,介绍他建立的一个自动的机器学习框架,几乎可以解决任何机器学习问题,项目很快也会发布出来。 这篇文章迅速火遍 Kaggle,他参加过100多个数据科学相关的竞赛,积累了很多宝贵的经验,看他很幽默...
- For small datasets, 'liblinear' is a good choice, whereas 'sag' and 'saga' are faster for large ones. - For multiclass problems, only 'newton-cg', 'sag', 'saga' and 'lbfgs' handle multinomial loss; 'liblinear' is limited to one-versus-rest schemes. - 'newton-cg', 'lbfgs' ...
clf_LoR = linear_model.LogisticRegression(C=1.0, penalty='l1', tol=1e-6) clf_LoR.fit(X, y) #LoR算法 class LogisticRegression Found at: sklearn.linear_model.logistic class LogisticRegression(BaseEstimator, LinearClassifierMixin, SparseCoefMixin): ...
There are some problems with LightBGM support for GPU: The three parameters, device, gpu_platform_id and gpu_device_id was a bit confusing for beginners. You can only get GPU support by installing lightbgm from the offical github repo, compile it from CMAKE, and go to their subdirectory ...
Kaggle competitions work by asking users or teams to provide solutions to well-defined problems. Competitors download the training and test files, train models on the labeled training file, generate predictions on the test file, and then upload a prediction file as a submission on...
To make LightGBM training faster, I attemped to setup LightBGM but met many problems. Apull requestis submitted with potentially more to come to Microsoft's LightBGM team. Thanks for reading! I would really appreciate any feedback. Happy coding, Alex Lialex@alexli.me ...
SyntaxError: Unexpected end of JSON input at https://www.kaggle.com/static/assets/app.js?v=0f4faa14a3317144d916:2:2934538 at https://www.kaggle.com/static/assets/app.js?v=0f4faa14a3317144d916:2:2930919 at Object.next (https://www.kaggle.com/static/assets/app.js?v=0f4faa14a3317144d...
Explore and run machine learning code with Kaggle Notebooks | Using data from Housing Prices, Portland, OR