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Spark MLlib

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Spark MLlib 一、Spark MLlib 模型选择与调参 CrossValidator TrainValidationSplit MLlib目录结构 MLlib处理流程 MLlib构成数据类型（Data Type）数学统计计算库机器学习管道（pipeline）机器学习算法二、Spark MLlib算法库 2.1 推荐算法（AlterNating Least Squares）(ALS) 2.2 ALS：Scala 部分内容原文地址：掘金：美图数据团队：从Spark MLlib到美图机器学习框架实践一、Spark MLlib 在 Spark 官网上展示了逻辑回归算法在 Spark 和 Hadoop 上运行性能比较，从下图可以看出 MLlib 比 MapReduce 快了 100 倍。 Spark MLlib 主要包括以下几方面的内容：学习算法：分类、回归、聚类和协同过滤；特征处理：特征提取、变换、降维和选择；管道(Pipeline)：用于构建、评估和调整机器学习管道的工具；持久性：保存和加载算法，模型和管道；实用工具：线性代数，统计，最优化，调参等工具。 Spark MLlib 典型流程如下：构造训练数据集构建各个 Stage Stage 组成 Pipeline 启动模型训练评估模型效果计算预测结果通过一个 Pipeline

How to solve “log4j:WARN No appenders could be found for logger” error on Twenty Newsgroups Classification Example

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问题 I am trying to run the 2newsgroup classification example in Mahout. I have set MAHOUT_LOCAL=true, the classifier doesn't display the Confusion matrix and gives the following warnings : ok. You chose 1 and we'll use cnaivebayes creating work directory at /tmp/mahout-work-cloudera + echo 'Preparing 20newsgroups data' Preparing 20newsgroups data + rm -rf /tmp/mahout-work-cloudera/20news-all + mkdir /tmp/mahout-work-cloudera/20news-all + cp -R /tmp/mahout-work-cloudera/20news-bydate/20news-bydate

R Warning: newdata' had 15 rows but variables found have 22 rows [duplicate]

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问题 This question already has answers here : Predict() - Maybe I'm not understanding it (4 answers) Closed 3 years ago . I have read few answers on this here but I am afraid I have not been able to figure out an answer. My R code is: colors <- bmw[bmw$Channel=="Colors" & bmw$Hour=20,] colors_test <- tail(colors, 89) colors_train <- head(colors, 810) colors_train_agg <- aggregate(colors_train$Impressions, list(colors_train$`Position of Ad in Break`), FUN=mean, na.rm=TRUE) colnames(colors_train_agg

ARIMA produced slope straight line

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问题 I am new to the time series with using the SARIMA model, and I followed the tutorial to build the model and trying to forecast the future trend. The thing goes well at the beginning but when produced the results it shows the slope straight line. and I build it on the Jupyter NoteBook First thing I checked my data, and make the data visually, but in fact, it seems the right data then I tried to change the values of P, D, Q and failed again https://github.com/Dongmingguoguo/Prediciton https:/

How to create a graph showing the predictive model, data and residuals in R

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问题 Given two variables, x and y , I run a dynlm regression on the variables and would like to plot the fitted model against one of the variables and the residual on the bottom showing how the actual data line differs from the predicting line. I've seen it done before and I've done it before, but for the life of me I can't remember how to do it or find anything that explains it. This gets me into the ballpark where I have a model and two variables, but I can't get the type of graph I want.

How to create a graph showing the predictive model, data and residuals in R

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How to create a graph showing the predictive model, data and residuals in R

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Appending predicted values and residuals to pandas dataframe

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问题 It's a useful and common practice to append predicted values and residuals from running a regression onto a dataframe as distinct columns. I'm new to pandas, and I'm having trouble performing this very simple operation. I know I'm missing something obvious. There was a very similar question asked about a year-and-a-half ago, but it wasn't really answered. The dataframe currently looks something like this: y x1 x2 880.37 3.17 23 716.20 4.76 26 974.79 4.17 73 322.80 8.70 72 1054.25 11.45 16 And

Predict using felm output with standard errors

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问题 Is there way to get predict behavior with standard errors from lfe::felm if the fixed effects are swept out using the projection method in felm ? This question is very similar to the question here, but none of the answers to that question can be used to estimate standard errors or confidence/prediction intervals. I know that there's currently no predict.felm , but I am wondering if there are workarounds similar to those linked above that might also work for estimating the prediction interval

Adding statsmodels 'predict' results to a Pandas dataframe

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问题 It is common to want to append the results of predictions to the dataset used to make the predictions, but the statsmodels predict function returns (non-indexed) results of a potentially different length than the dataset on which predictions are based. For example, if the test dataset, test , contains any null entries, then mod_fit = sm.Logit.from_formula('Y ~ A B C', train).fit() press = mod_fit.predict(test) will produce an array that is shorter than the length of test , and cannot be