classification

问题 Can anyone explain how to interpret coefficientMatrix , interceptVector , Confusion matrix of a multinomial logistic regression . According to Spark documentation: Multiclass classification is supported via multinomial logistic (softmax) regression. In multinomial logistic regression, the algorithm produces K sets of coefficients, or a matrix of dimension K×J where K is the number of outcome classes and J is the number of features. If the algorithm is fit with an intercept term then a length

问题 Can anyone explain how to interpret coefficientMatrix , interceptVector , Confusion matrix of a multinomial logistic regression . According to Spark documentation: Multiclass classification is supported via multinomial logistic (softmax) regression. In multinomial logistic regression, the algorithm produces K sets of coefficients, or a matrix of dimension K×J where K is the number of outcome classes and J is the number of features. If the algorithm is fit with an intercept term then a length

问题 Can anyone explain how to interpret coefficientMatrix , interceptVector , Confusion matrix of a multinomial logistic regression . According to Spark documentation: Multiclass classification is supported via multinomial logistic (softmax) regression. In multinomial logistic regression, the algorithm produces K sets of coefficients, or a matrix of dimension K×J where K is the number of outcome classes and J is the number of features. If the algorithm is fit with an intercept term then a length

问题 I am quite new to tensorflow, I would like to clearly know, what does the below command do? import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers import os num_skipped = 0 for folder_name in ("Cat", "Dog"): print("folder_name:",folder_name) #folder_name: Cat folder_path = os.path.join("Dataset/PetImages", folder_name) print("folder_path:",folder_path) #folder_path: Dataset/PetImages/Cat for fname in os.listdir(folder_path): print("fname:",fname) #fname: 5961

问题 I have a data frame with 4 different groups of features. I need to create 4 different models with these four different feature groups and combine them with the ensemble voting classifier. Furthermore, I need to test the classifier using k-fold cross validation. However, I am finding it difficult to combine different feature sets, voting classifier and k-fold cross validation with functionality available in sklearn. Following is the code that I have so far. y = df1.index x = preprocessing

问题 I am using the caret package to train models for a classification problem. I know that defaultSummary can be used to calculate Accuracy/Kappa (and SDs), and twoClassSummary will calculate Sens/Spec. I would like to also calculate positive and negative predictive values (PPV/NPV, and SDs) as easily as possible, in the same fashion. I have come up with a solution, but wonder if anyone could confirm that the solution appears reasonable. First, I generate the predictive values: predictiveValues <

问题 With regard to this link Predicted probabilities in R ranger package, I have a question. Imagine I have a mixed data frame, df (comprising of factor and numeric variables) and I want to do classification using ranger. I am splitting this data frame as test and train sets as Train_Set and Test_Set. BiClass is my prediction factor variable and comprises of 0 and 1 (2 levels) I want to calculate and attach class probabilities to the data frame using ranger using the following commands: Biclass

问题 I have a question concerning the construction of confusion matrix from the below link: Ranger Predicted Class Probability of each row in a data frame If I have the following code for example (as explained by the answer in the link): library(ranger) library(caret) idx = sample(nrow(iris),100) data = iris data$Species = factor(ifelse(data$Species=="versicolor",1,0)) Train_Set = data[idx,] Test_Set = data[-idx,] mdl <- ranger(Species ~ ., ,data=Train_Set,importance="impurity", save.memory = TRUE

问题 I wanted to leverage this answer How to plot scikit learn classification report? turning an sklearn classification report into a heatmap. It's all working with their sample report, however my classification report looks slightly different and is thus screwing up the functions. Their report (notice the avg / total): sampleClassificationReport = precision recall f1-score support Acacia 0.62 1.00 0.76 66 Blossom 0.93 0.93 0.93 40 Camellia 0.59 0.97 0.73 67 Daisy 0.47 0.92 0.62 272 Echium 1.00 0

问题 I wanted to leverage this answer How to plot scikit learn classification report? turning an sklearn classification report into a heatmap. It's all working with their sample report, however my classification report looks slightly different and is thus screwing up the functions. Their report (notice the avg / total): sampleClassificationReport = precision recall f1-score support Acacia 0.62 1.00 0.76 66 Blossom 0.93 0.93 0.93 40 Camellia 0.59 0.97 0.73 67 Daisy 0.47 0.92 0.62 272 Echium 1.00 0