confusion-matrix

问题 I have a code that can print the confusion matrix for a multiclass classification problem. import itertools import numpy as np import matplotlib.pyplot as plt from sklearn import svm, datasets from sklearn.model_selection import train_test_split from sklearn.metrics import confusion_matrix # import some data to play with iris = datasets.load_iris() X = iris.data y = iris.target class_names = iris.target_names # Split the data into a training set and a test set X_train, X_test, y_train, y_test

问题 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

问题 I'm using Python and have some confusion matrixes. I'd like to calculate precisions and recalls and f-measure by confusion matrixes in multiclass classification. My result logs don't contain y_true and y_pred , just contain confusion matrix. Could you tell me how to get these scores from confusion matrix in multiclass classification? 回答1: Let's consider the case of MNIST data classification (10 classes), where for a test set of 10,000 samples we get the following confusion matrix cm (Numpy

问题 Sklearn.metrics has great functions for obtaining classification metrics, although something that I think is missing is a function to return the TP, FN, FP and FN counts given the predicted and actual label sequences. Or even from the confusion matrix. I know it's possible to obtain the confusion matrix using sklearn , but I need the actual TP, FN, FP and FN counts (for multilabel classification - more than 2 labels), and to obtain those counts for each of the classes. So say, I have the

问题 I have a dataframe with 3000 rows and 3 columns as follows: 0 col1 col2 col3 ID1 1 0 1 Id2 1 1 0 Id3 0 1 1 Id4 2 1 0 Id5 2 2 3 … .. .. .. Id3000 3 1 0 In this data frame, the value of each column and row refers to a result of a prediction problem as follows: 0 means TP, 1 means FP, 2 refers to TN and 3 points to FN in each column. So I want to calculate the accuracy of each column. something like this: Accuracy result: col1 col2 col3 0.67 0.68 0.79 Any idea that I can calculate the important

问题 I have a dataframe with 3000 rows and 3 columns as follows: 0 col1 col2 col3 ID1 1 0 1 Id2 1 1 0 Id3 0 1 1 Id4 2 1 0 Id5 2 2 3 … .. .. .. Id3000 3 1 0 In this data frame, the value of each column and row refers to a result of a prediction problem as follows: 0 means TP, 1 means FP, 2 refers to TN and 3 points to FN in each column. So I want to calculate the accuracy of each column. something like this: Accuracy result: col1 col2 col3 0.67 0.68 0.79 Any idea that I can calculate the important

问题 I'm currently working on a Random Forest Classification model which contains 24,000 samples where 20,000 of them belong to class 0 and 4,000 of them belong to class 1 . I made a train_test_split where test_set is 0.2 of the whole dataset (around 4,800 samples in test_set ). Since I'm dealing with imbalanced data, I looked at the hyperparameter class_weight which is aimed to solve this issue. The problem I'm facing the moment I'm setting class_weight='balanced' and look at the confusion_matrix

问题 I have this data frame called conf_mat with two columns including predicted values and reference values in each objects. I have 20 objects in this dataframe. dput(Conf_mat) structure(list(Predicted = c(100, 200, 200, 100, 100, 200, 200, 200, 100, 200, 500, 100, 100, 100, 100, 100, 100, 100, 500, 200 ), Reference = c(600, 200, 200, 200, 200, 200, 200, 200, 500, 500, 500, 200, 200, 200, 200, 200, 200, 200, 200, 200)), .Names = c("Predicted", "Reference"), row.names = c(NA, 20L), class = "data

问题 I am computing my confusion matrix as shown below for image semantic segmentation which is a pretty verbose approach: def confusion_matrix(preds, labels, conf_m, sample_size): preds = normalize(preds,0.9) # returns [0,1] tensor preds = preds.flatten() labels = labels.flatten() for i in range(len(preds)): if preds[i]==1 and labels[i]==1: conf_m[0,0] += 1/(len(preds)*sample_size) # TP elif preds[i]==1 and labels[i]==0: conf_m[0,1] += 1/(len(preds)*sample_size) # FP elif preds[i]==0 and labels[i

问题 I was running a random forest classification model and initially divided the data into train (80%) and test (20%). However, the prediction had too many False Positive which I think was because there was too much noise in training data, so I decided to split the data in a different method and here's how I did it. Since I thought the high False Positive was due to the noise in the train data, I made the train data to have the equal number of target variables. For example, if I have data of 10