multilabel-classification

问题 I have a dataframe where one column is short_names . short_names consist of 2-5 letters of names => BG , OP , LE , WEL , LC . Each row can have any number of names. I am trying to use MultiLabelBinarizer to convert the names into individual columns such that if the rows have similar names then there will be 1 in the columns one_hot = MultiLabelBinarizer() one_hot.fit_transform(df['short_name']) one_hot.classes__ Because there is a '-' in one the rows which result in an error TypeError: 'float

问题 I have a dataframe where one column is short_names . short_names consist of 2-5 letters of names => BG , OP , LE , WEL , LC . Each row can have any number of names. I am trying to use MultiLabelBinarizer to convert the names into individual columns such that if the rows have similar names then there will be 1 in the columns one_hot = MultiLabelBinarizer() one_hot.fit_transform(df['short_name']) one_hot.classes__ Because there is a '-' in one the rows which result in an error TypeError: 'float

问题 I try to calculate the f1_score but I get some warnings for some cases when I use the sklearn f1_score method. I have a multilabel 5 classes problem for a prediction. import numpy as np from sklearn.metrics import f1_score y_true = np.zeros((1,5)) y_true[0,0] = 1 # => label = [[1, 0, 0, 0, 0]] y_pred = np.zeros((1,5)) y_pred[:] = 1 # => prediction = [[1, 1, 1, 1, 1]] result_1 = f1_score(y_true=y_true, y_pred=y_pred, labels=None, average="weighted") print(result_1) # prints 1.0 result_2 = f1

问题 I try to calculate the f1_score but I get some warnings for some cases when I use the sklearn f1_score method. I have a multilabel 5 classes problem for a prediction. import numpy as np from sklearn.metrics import f1_score y_true = np.zeros((1,5)) y_true[0,0] = 1 # => label = [[1, 0, 0, 0, 0]] y_pred = np.zeros((1,5)) y_pred[:] = 1 # => prediction = [[1, 1, 1, 1, 1]] result_1 = f1_score(y_true=y_true, y_pred=y_pred, labels=None, average="weighted") print(result_1) # prints 1.0 result_2 = f1

问题 I am working on a multi-label text classification problem (Total target labels 90). The data distribution has a long tail and class imbalance and around 100k records. I am using the OAA strategy (One against all). I am trying to create an ensemble using Stacking. Text features : HashingVectorizer (number of features 2**20, char analyzer) TSVD to reduce the dimensionality (n_components=200). text_pipeline = Pipeline([ ('hashing_vectorizer', HashingVectorizer(n_features=2**20, analyzer='char'))

问题 I tried myself but couldn't reach the final point that's why posting here, please guide me. I am working in multi-label image classification and have slightly different scenarios. Actually I am confused, how we will map labels and their attribute with Id etc So we can use for training and testing. Here is code on which I am working import os import numpy as np import pandas as pd from keras.utils import to_categorical from collections import Counter from keras.callbacks import Callback from

问题 I tried myself but couldn't reach the final point that's why posting here, please guide me. I am working in multi-label image classification and have slightly different scenarios. Actually I am confused, how we will map labels and their attribute with Id etc So we can use for training and testing. Here is code on which I am working import os import numpy as np import pandas as pd from keras.utils import to_categorical from collections import Counter from keras.callbacks import Callback from

问题 I tried myself but couldn't reach the final point that's why posting here, please guide me. I am working in multi-label image classification and have slightly different scenarios. Actually I am confused, how we will map labels and their attribute with Id etc So we can use for training and testing. Here is code on which I am working import os import numpy as np import pandas as pd from keras.utils import to_categorical from collections import Counter from keras.callbacks import Callback from

来源： https://stackoverflow.com/questions/63295505/keras-classification-clarification

来源： https://stackoverflow.com/questions/53037451/keras-custom-metrics-for-multi-label-classfication