One possible approach, instead of using OneVsRestClassifier which is for multi-class tasks, is to use MultiOutputClassifier from the sklearn.multioutput module. Below is a small reproducible sample code with the number of input features and target outputs requested by the OP import xgboost as xgb from sklearn.datasets import make_multilabel_classification from sklearn.model_selection import train_test_split from sklearn.multioutput import … Read more
For multi-label classification you have two ways to go First consider the following. is the number of examples. is the ground truth label assignment of the example.. is the example. is the predicted labels for the example. Example based The metrics are computed in a per datapoint manner. For each predicted label its only its … Read more
Change relu to sigmoid of output layer. Modify cross entropy loss to explicit mathematical formula of sigmoid cross entropy loss (explicit loss was working in my case/version of tensorflow ) import tensorflow as tf # hidden Layer class HiddenLayer(object): def __init__(self, input, n_in, n_out): self.input = input w_h = tf.Variable(tf.random_normal([n_in, n_out],mean = 0.0,stddev = 0.05)) … Read more
Multiclass classification To better illustrate the differences, let us assume that your goal is that of classifying SO questions into n_classes different, mutually exclusive classes. For the sake of simplicity in this example we will only consider four classes, namely ‘Python’, ‘Java’, ‘C++’ and ‘Other language’. Let us assume that you have a dataset formed … Read more
You need to add the ‘average’ param. According to the documentation: average : string, [None, ‘binary’ (default), ‘micro’, ‘macro’, ‘samples’, ‘weighted’] This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data: Do this: print(“Precision Score : “,precision_score(y_test, y_pred, … Read more
In short Don’t use softmax. Use sigmoid for activation of your output layer. Use binary_crossentropy for loss function. Use predict for evaluation. Why In softmax when increasing score for one label, all others are lowered (it’s a probability distribution). You don’t want that when you have multiple labels. Complete Code from tensorflow.keras.models import Sequential from … Read more