confusion-matrix

I am using R v3.3.2 and Caret 6.0.71 (i.e. latest versions) to construct a logistic regression classifier. I am using the confusionMatrix function to create stats for judging its performance. logRegConfMat <- confusionMatrix(logRegPrediction, valData[,"Seen"]) Reference 0, Prediction 0 = 30 Reference 1, Prediction 0 = 14 Reference 0, Prediction 1 = 60 Reference 1, Prediction 1 = 164 Accuracy : 0.7239 Sensitivity : 0.3333 Specificity : 0.9213 The target value in my data (Seen) uses 1 for true and 0 for false. I assume the Reference (Ground truth) columns and Predication (Classifier) rows in the

How do you compute the true- and false- positive rates of a multi-class classification problem? Say, y_true = [1, -1, 0, 0, 1, -1, 1, 0, -1, 0, 1, -1, 1, 0, 0, -1, 0] y_prediction = [-1, -1, 1, 0, 0, 0, 0, -1, 1, -1, 1, 1, 0, 0, 1, 1, -1] The confusion matrix is computed by metrics.confusion_matrix(y_true, y_prediction) , but that just shifts the problem. EDIT after @seralouk's answer. Here, the class -1 is to be considered as the negatives, while 0 and 1 are variations of positives. Using your data, you can get all the metrics for all the classes at once: import numpy as np from sklearn

I am trying to estimate a logistic regression, using the 10-fold cross-validation. #import libraries library(car); library(caret); library(e1071); library(verification) #data import and preparation data(Chile) chile <- na.omit(Chile) #remove "na's" chile <- chile[chile$vote == "Y" | chile$vote == "N" , ] #only "Y" and "N" required chile$vote <- factor(chile$vote) #required to remove unwanted levels chile$income <- factor(chile$income) # treat income as a factor Goal is to estimate a glm - model that predicts to outcome of vote "Y" or "N" depended on relevant explanatory variables and, based on

I have a lot of .csv files with the following format. 338,800 338,550 339,670 340,600 327,500 301,430 299,350 284,339 284,338 283,335 283,330 283,310 282,310 282,300 282,300 283,290 From column 1, I wanted to read current row and compare it with the value of the previous row. If it is greater OR equal, continue comparing and if the value of the current cell is smaller than the previous row - then i divide the current value with the previous value and proceed. For example in the table given above: the smaller value we will get depending on my requirement from Column 1 is 327 (because 327 is

When I print out scikit-learn's confusion matrix, I receive a very huge matrix. I want to analyze what are the true positives, true negatives etc. How can I do so? This is how my confusion matrix looks like. I wish to understand this better. [[4015 336 0 ..., 0 0 2] [ 228 2704 0 ..., 0 0 0] [ 4 7 19 ..., 0 0 0] ..., [ 3 2 0 ..., 5 0 0] [ 1 1 0 ..., 0 0 0] [ 13 1 0 ..., 0 0 11]] IIUC, your question is undefined. "False positives", "true negatives" - these are terms that are defined only for binary classification. Read more about the definition of a confusion matrix . In this case, the confusion

I have multi-class classification using RNN and here is my main code for RNN: def RNN(x, weights, biases): x = tf.unstack(x, input_size, 1) lstm_cell = rnn.BasicLSTMCell(num_unit, forget_bias=1.0, state_is_tuple=True) stacked_lstm = rnn.MultiRNNCell([lstm_cell]*lstm_size, state_is_tuple=True) outputs, states = tf.nn.static_rnn(stacked_lstm, x, dtype=tf.float32) return tf.matmul(outputs[-1], weights) + biases logits = RNN(X, weights, biases) prediction = tf.nn.softmax(logits) cost =tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=Y)) optimizer = tf.train