classification

来源： https://stackoverflow.com/questions/47665417/mlp-with-partial-fit-performing-worse-than-with-fit-in-a-supervised-classifi

来源： https://stackoverflow.com/questions/47665417/mlp-with-partial-fit-performing-worse-than-with-fit-in-a-supervised-classifi

来源： https://stackoverflow.com/questions/63749263/different-results-for-svm-using-caret-in-r-when-classprobs-true

来源： https://stackoverflow.com/questions/47185586/logistic-regression-valueerror-classification-metrics-cant-handle-a-mix-of-c

问题 I have the following simple neural network (with 1 neuron only) to test the computation precision of sigmoid activation & binary_crossentropy of Keras: model = Sequential() model.add(Dense(1, input_dim=1, activation='sigmoid')) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) To simplify the test, I manually set the only weight to 1 and bias to 0, and then evaluate the model with 2-point training set {(-a, 0), (a, 1)} , i.e. y = numpy.array([0, 1]) for a in

问题 I have a training data set of 20 column , all of which are factors which i have to use for training a model, I have been given test data set on which I have to apply my model for predictions and submit. I was doing initial data exploration and just out of curiosity checked the levels of training data and testing data levels since we are dealing with all categorical variables.To my dismay most of the categories (variables) have different levels in training and testing data set. for example

问题 I have a training data set of 20 column , all of which are factors which i have to use for training a model, I have been given test data set on which I have to apply my model for predictions and submit. I was doing initial data exploration and just out of curiosity checked the levels of training data and testing data levels since we are dealing with all categorical variables.To my dismay most of the categories (variables) have different levels in training and testing data set. for example

问题 I have about a 30% and 70% for class 0 (minority class) and class 1 (majority class). Since I do not have a lot of data, I am planning to oversample the minority class to balance out the classes to become a 50-50 split. I was wondering if oversampling should be done before or after splitting my data into train and test sets. I have generally seen it done before splitting in online examples, like this: df_class0 = train[train.predict_var == 0] df_class1 = train[train.predict_var == 1] df

问题 I have about a 30% and 70% for class 0 (minority class) and class 1 (majority class). Since I do not have a lot of data, I am planning to oversample the minority class to balance out the classes to become a 50-50 split. I was wondering if oversampling should be done before or after splitting my data into train and test sets. I have generally seen it done before splitting in online examples, like this: df_class0 = train[train.predict_var == 0] df_class1 = train[train.predict_var == 1] df

问题 I have about a 30% and 70% for class 0 (minority class) and class 1 (majority class). Since I do not have a lot of data, I am planning to oversample the minority class to balance out the classes to become a 50-50 split. I was wondering if oversampling should be done before or after splitting my data into train and test sets. I have generally seen it done before splitting in online examples, like this: df_class0 = train[train.predict_var == 0] df_class1 = train[train.predict_var == 1] df