pca

问题 I have multi class labels and want to compute the accuracy of my model. I am kind of confused on which sklearn function I need to use. As far as I understood the below code is only used for the binary classification. # dividing X, y into train and test data X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25,random_state = 0) # training a linear SVM classifier from sklearn.svm import SVC svm_model_linear = SVC(kernel = 'linear', C = 1).fit(X_train, y_train) svm

问题 G'day. I am plotting a pca with the factoextra package. I have 3 points for each factor and would like to draw ellipses around each. But I am getting the error Too few points to calculate an ellipse . It is possible to draw ellipses around 3 points in ggplot2 with the stat_ellipse function. I can confirm this by looking at the calculate_ellipse code from ggplot2 that says else if (dfd < 3) {message("Too few points to calculate an ellipse") . So what ellipse function is factoextra using in

问题 I'm implementing a classification task with binary outcome using RandomForestClassifier and I know the importance of data preprocessing to improve the accuracy score. In particular, my dataset contains more than 100 features and almost 4000 instances and I want to perform a dimensionality reduction technique in order to avoid overfitting since there is an high presence of noise in the data. For these tasks I usually use a classical Feature Selection method (filters, wrappers, feature

问题 I'm wondering if anyone knows of how to implement a rolling/moving window PCA on a pandas dataframe. I've looked around and found implementations in R and MATLAB but not Python. Any help would be appreciated! This is not a duplicate - moving window PCA is not the same as PCA on the entire dataframe. Please see pandas.DataFrame.rolling() if you do not understand the difference 回答1: Unfortunately, pandas.DataFrame.rolling() seems to flatten the df before rolling, so it cannot be used as one

问题 When doing PCA in R, p <- princomp(iris[,1:4]) I conclude different Components' coefficients by the following two methods: IrisLoading <- p$loadings[,1:2] #methods1, use the fist two Comp. it results like this Comp.1 Comp.2 Sepal.Length 0.36138659 -0.65658877 Sepal.Width -0.08452251 -0.73016143 Petal.Length 0.85667061 0.17337266 Petal.Width 0.35828920 0.07548102 Then if I only View its Loadings by p$loadings the result is Loadings: Comp.1 Comp.2 Comp.3 Comp.4 Sepal.Length 0.361 -0.657 -0.582

问题 When doing PCA in R, p <- princomp(iris[,1:4]) I conclude different Components' coefficients by the following two methods: IrisLoading <- p$loadings[,1:2] #methods1, use the fist two Comp. it results like this Comp.1 Comp.2 Sepal.Length 0.36138659 -0.65658877 Sepal.Width -0.08452251 -0.73016143 Petal.Length 0.85667061 0.17337266 Petal.Width 0.35828920 0.07548102 Then if I only View its Loadings by p$loadings the result is Loadings: Comp.1 Comp.2 Comp.3 Comp.4 Sepal.Length 0.361 -0.657 -0.582

问题 This question is very similar to preprocess within cross-validation in caret; however, in a project that i'm working on I would only like to do PCA on three predictors out of 19 in my case. Here is the example from preprocess within cross-validation in caret and I'll use this data ( PimaIndiansDiabetes ) for ease (this is not my project data but concept should be the same). I would then like to do the preProcess only on a subset of variables i.e. PimaIndiansDiabetes[, c(4,5,6)]. Is there a

问题 I am doing a principal component analysis on 5 variables within a dataframe to see which ones I can remove. df <-data.frame(variableA, variableB, variableC, variableD, variableE) prcomp(scale(df)) summary(prcomp) gives the following results PC1 PC2 PC3 PC4 PC5 Proportion of Variance 0.5127 0.2095 0.1716 0.06696 0.03925 My issue is that if I change the order of the variabes in the dataframe, I get the same results df <-data.frame(variableC, variableF, variableA, variableE, variableB) prcomp

问题 I'm trying to do a PCR with tidymodels however i'm keep runing into this problem. I know there is a similar post but the solution over there, doesn't work form my case. My data library(AppliedPredictiveModeling) data(solubility) train = solTrainY %>% bind_cols(solTrainXtrans) %>% rename(solubility = ...1) My PCR analysis train %<>% mutate_all(., as.numeric) %>% glimpse() tidy_rec = recipe(solubility ~ ., data = train) %>% step_corr(all_predictors(), threshold = 0.9) %>% step_pca(all

问题 This question is very similar to preprocess within cross-validation in caret; however, in a project that i'm working on I would only like to do PCA on three predictors out of 19 in my case. Here is the example from preprocess within cross-validation in caret and I'll use this data ( PimaIndiansDiabetes ) for ease (this is not my project data but concept should be the same). I would then like to do the preProcess only on a subset of variables i.e. PimaIndiansDiabetes[, c(4,5,6)]. Is there a