linear-regression

问题 I've searched several similar topics covering comparable problems. For example this, this and this, among others. Despite this, I still haven't managed to solve my issue, why I now try to ask the community. What I'm ultimately trying to do is with a CNN and regression predict three parameters. The inputs are matrices (and can now be plotted as RGB images after I've pre-processed them in several steps) with the initial size of (3724, 4073, 3). Due to the size of the data set I'm feeding the

问题 I was trying to build a multiple regression model to predict housing prices using the following features: [bedrooms bathrooms sqft_living view grade] = [0.09375 0.266667 0.149582 0.0 0.6] I have standardized and scaled the features using sklearn.preprocessing.MinMaxScaler . I used Keras to build the model: def build_model(X_train): model = Sequential() model.add(Dense(5, activation = 'relu', input_shape = X_train.shape[1:])) model.add(Dense(1)) optimizer = Adam(lr = 0.001) model.compile(loss

问题 Why do we take one less dummy variable than the total number of dummy variables in a Multiple Linear regression model? Like, if the model contains 4 dummy variables, we update our features vector for training our regression model. x = x[:, 1:4] . 回答1: Because of the Dummy Variable Trap. By including dummy variable in a regression model however, one should be careful of the Dummy Variable Trap. The Dummy Variable trap is a scenario in which the independent variables are multicollinear - a

问题 Is there a way to directly plot model average summary outputs from MuMIn model.avg() for different variables with confidence bands. Previously I had been using ggplot and ggpredict to plot terms from the actual models, but I haven't been able to find a way to plot the results of the averaged models. Clearly I can plot the slope and intercept manually, but getting accurate confidence bands and plotting from confint() is not ideal and I have yet to get confidence bands from the intervals that

问题 I am using scikit and using mean_squared_error as a scoring function for model evaluation in cross_val_score. rms_score = cross_validation.cross_val_score(model, X, y, cv=20, scoring='mean_squared_error') I am using mean_squared_error as it is a regression problem and the estimators (model) used are lasso , ridge and elasticNet . For all these estimators, I am getting rms_score as negative values. How is it possible, given the fact that the differences in y values are squared. 回答1: You get

问题 I know that it's possible to export models as PMML with Spark-MLlib , but what about Spark-ML ? Is it possible to convert LinearRegressionModel from org.apache.spark.ml.regression to a LinearRegressionModel from org.apache.spark.mllib.regression to be able to invoke the toPMML() method? 回答1: You can convert Spark ML pipelines to PMML using the JPMML-SparkML library: StructType schema = dataFrame.schema() PipelineModel pipelineModel = pipeline.fit(dataFrame); org.dmg.pmml.PMML pmml = org.jpmml

问题 I know that it's possible to export models as PMML with Spark-MLlib , but what about Spark-ML ? Is it possible to convert LinearRegressionModel from org.apache.spark.ml.regression to a LinearRegressionModel from org.apache.spark.mllib.regression to be able to invoke the toPMML() method? 回答1: You can convert Spark ML pipelines to PMML using the JPMML-SparkML library: StructType schema = dataFrame.schema() PipelineModel pipelineModel = pipeline.fit(dataFrame); org.dmg.pmml.PMML pmml = org.jpmml

问题 I am using the R package segmented to calculate parameters for a model, in which the response variable is linearly correlated with the explanatory variable until a breakpoint, then the response variable becomes independent from the explanatory variable. In other words, a segmented linear model with the second part having a slope = 0. What I already did is: linear1 <- lm(Y ~ X) linear2 <- segmented (linear1, seg.Z = ~ X, psi = 2) This gives a model that have a very good first line, but the

问题 I am trying to understand how R determines reference groups for interactions in a linear model. Consider the following: df <- structure(list(id = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L), .Label = c("1", "2", "3", "4", "5"), class = "factor"), year = structure(c(1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L), .Label = c("1", "2"),

问题 I am a beginner with R. I have a data set on air pollution. The columns are site, measured concentration and 80 variables (v1-v80) that might influence the concentration. I want to make a model with forward stepwise regression based on R-squared/adj with my own code (so I do not want to use something like step() or regsubset()). The dependent variable is concentration and the variables v1-v80 as independent variables. I wrote the following code for the first step (data set is simplified):