3、SKlearn 中的聚类方法 SKlearn 工具包提供了多种聚类分析算法:原型聚类方法(Prototype)、密度聚类方法(Density)、层次聚类方法(Hierarchical)、模型聚类(Model),等等,原型聚类方法又包括 k均值算法(K-Means)、学习向量量化算法(LVQ)、高斯混合算法(Gaussian Mixture)。详见下表。 为什么会有这么多方法和算法呢?因为...
在Data Analysis Using Regression and Multilevel/Hierarchical Models(Cambridge University Press, 2007, p.56) 中,Gelman and Hill建议在数据集中既有连续型自变量又有二值型自变量的情况下,用两杯标准差去除,而不是用一倍标准差。这样的话,标准化自变量一个单位的变化就对应于均值上下一个标准差的变化。 ... ...
简单例子 fromsklearn.linear_modelimportLogisticRegressionfromsklearn.model_selectionimporttrain_test_splitfromsklearn.metricsimportaccuracy_score# 假设 X 是特征矩阵,y 是标签向量X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)# 创建逻辑回归模型model =...
As you have probably guessed from the name, statsmodels is a library forPythonthat enables its users to conduct data exploration via the use of various methods of estimation of statistical models and performing statistical assertions and analysis. Among many useful features are descriptive and result ...
from pyspark.sql import SparkSession from pyspark.ml.feature import VectorAssembler from pyspark.ml.regression import LinearRegression from pyspark.ml.tuning import ParamGridBuilder, CrossValidator from pyspark.ml.evaluation import RegressionEvaluator from statsmodels.tsa.arima_model import ARIMA 创建SparkSessio...
链接:statsmodels/statsmodels 8.Shogun 是一种提供大量高效且统一的机器学习(ML)方法的机器学习工具箱。它能容易地把多种数据表示,算法类和通用工具紧密地联系起来。 提交:15172,贡献者:105 链接:shogun-toolbox/shogun 9.Chainer 是一个基于 Python 并且独立的深度学习模型开源框架。Chainer 提供一种灵活、直观且高...
[015]3.3 Multiple Linear Regression.zh_en 15:38 [016]3.4 Some important questions.zh_en 14:52 [017]3.5 Extensions of the Linear Model.zh_en 14:17 [018]3.Py Linear Regression and statsmodels Package I 2023.zh_en 09:10 [019]3.Py Multiple Linear Regression Package I 2023.zh_en ...
With Statsmodels, users can perform linear and non-linear regression, time-series analysis, and hypothesis testing, among other statistical tasks. It offers a wide array of statistical models and methods, allowing researchers and data scientists to explore and analyze data with confidence. For profe...
statsmodels 0.14.0 Statistical computations and models for Python streamlit 1.23.1 The fastest way to build data apps in Python streamz 0.6.3 Streams supersmoother 0.4 Python implementation of Friedman's Supersmoother swifter 1.3.4 A package which efficiently applies any function to a pandas datafra...
statsmodels - Time series analysis, seasonal decompose example, SARIMA, granger causality. darts - Time Series library (LightGBM, Neural Networks). kats - Time series prediction library by Facebook. prophet - Time series prediction library by Facebook. neural_prophet - Time series prediction built ...