predict

来源： https://stackoverflow.com/questions/64096043/i-cant-make-a-vecm-prediction-with-tsdyn-package-number-of-test-data-rows-diff

来源： https://stackoverflow.com/questions/63840397/whats-the-purpose-of-encoding-if-i-cannot-use-it-in-predict

问题 I have a problem when I use my model to do some prediction, R shows this message Warning message prediction from a rank-deficient fit may be misleading , how can I solve it? I think my model is correct is the prediction that fails and I don't know why. Here you can see step by step what I am doing and the summary of model: myModel <- lm(margin~.,data = dataClean[train,c(target,numeric,categoric)]) Call: lm(formula = margin ~ ., data = dataClean[train, c(target, numeric, categoric)]) Residuals

问题 I have a problem when I use my model to do some prediction, R shows this message Warning message prediction from a rank-deficient fit may be misleading , how can I solve it? I think my model is correct is the prediction that fails and I don't know why. Here you can see step by step what I am doing and the summary of model: myModel <- lm(margin~.,data = dataClean[train,c(target,numeric,categoric)]) Call: lm(formula = margin ~ ., data = dataClean[train, c(target, numeric, categoric)]) Residuals

问题 I have a model in R that includes a significant three-way interaction between two continuous independent variables IVContinuousA, IVContinuousB, IVCategorical and one categorical variable (with two levels: Control and Treatment). The dependent variable is continuous (DV). model <- lm(DV ~ IVContinuousA * IVContinuousB * IVCategorical) You can find the data here I am trying to find out a way to visualise this in R to ease my interpretation of it (perhaps in ggplot2 ?). Somewhat inspired by

问题 I have a model in R that includes a significant three-way interaction between two continuous independent variables IVContinuousA, IVContinuousB, IVCategorical and one categorical variable (with two levels: Control and Treatment). The dependent variable is continuous (DV). model <- lm(DV ~ IVContinuousA * IVContinuousB * IVCategorical) You can find the data here I am trying to find out a way to visualise this in R to ease my interpretation of it (perhaps in ggplot2 ?). Somewhat inspired by

问题 I am getting an error when use gradient boosting model in python. I previously normalized the data, used VectorAssemble to transform, and indexed the columns, error occurs when when I run this: from pyspark.ml import Pipeline #pipeline = Pipeline(stages=[gbt]) stages = [] stages += [gbt] pipeline = Pipeline(stages=stages) model = pipeline.fit(df_train) prediction = model.transform(df_train) prediction.printSchema() this is the error: command-3539065191562733> in <module>() 6 7 pipeline =

问题 I know the general rule that we should test a trained classifier only on the testing set. But now comes the question: When I have an already trained and tested classifier ready, can I apply it to the same dataset that was the base of the training and testing set? Or do I have to apply it to a new predicting set that is different from the training+testing set? And what if I predict a label column of a time series (edited later: I do not mean to create a classical time series analysis here, but

问题 I know the general rule that we should test a trained classifier only on the testing set. But now comes the question: When I have an already trained and tested classifier ready, can I apply it to the same dataset that was the base of the training and testing set? Or do I have to apply it to a new predicting set that is different from the training+testing set? And what if I predict a label column of a time series (edited later: I do not mean to create a classical time series analysis here, but

问题 I am trying to retain an ID on the row when predicting using a Random Forest model to merge back on to the original dataframe. I am using step_naomit in the recipe that removes the rows with missing data when I bake the training data, but also removes the records with missing data on the testing data. Unfortunately, I don't have an ID to easily know which records were removed so I can accurately merge back on the predictions. I have tried to add an ID column to the original data, but bake