train-test-split

问题 I am using datatable dataframe. How can I split the dataframe into train and test dataset? Similarly to pandas dataframe, I tried to use train_test_split(dt_df,classes) from sklearn.model_selection, but it doesn't work and I get error. import datatable as dt import numpy as np from sklearn.model_selection import train_test_split dt_df = dt.fread(csv_file_path) classe = dt_df[:, "classe"]) del dt_df[:, "classe"]) X_train, X_test, y_train, y_test = train_test_split(dt_df, classe, test_size=test

问题 I am using datatable dataframe. How can I split the dataframe into train and test dataset? Similarly to pandas dataframe, I tried to use train_test_split(dt_df,classes) from sklearn.model_selection, but it doesn't work and I get error. import datatable as dt import numpy as np from sklearn.model_selection import train_test_split dt_df = dt.fread(csv_file_path) classe = dt_df[:, "classe"]) del dt_df[:, "classe"]) X_train, X_test, y_train, y_test = train_test_split(dt_df, classe, test_size=test

问题 On a jupyter notebook with Tensorflow-2.0.0, a train-validation-test split of 80-10-10 was performed in this way: import tensorflow_datasets as tfds from os import getcwd splits = tfds.Split.ALL.subsplit(weighted=(80, 10, 10)) filePath = f"{getcwd()}/../tmp2/" splits, info = tfds.load('fashion_mnist', with_info=True, as_supervised=True, split=splits, data_dir=filePath) However, when trying to run the same code locally I get the error AttributeError: type object 'Split' has no attribute 'ALL'

问题 On a jupyter notebook with Tensorflow-2.0.0, a train-validation-test split of 80-10-10 was performed in this way: import tensorflow_datasets as tfds from os import getcwd splits = tfds.Split.ALL.subsplit(weighted=(80, 10, 10)) filePath = f"{getcwd()}/../tmp2/" splits, info = tfds.load('fashion_mnist', with_info=True, as_supervised=True, split=splits, data_dir=filePath) However, when trying to run the same code locally I get the error AttributeError: type object 'Split' has no attribute 'ALL'

问题 I have the following data: pd.DataFrame({'Group_ID':[1,1,1,2,2,2,3,4,5,5], 'Item_id':[1,2,3,4,5,6,7,8,9,10], 'Target': [0,0,1,0,1,1,0,0,0,1]}) Group_ID Item_id Target 0 1 1 0 1 1 2 0 2 1 3 1 3 2 4 0 4 2 5 1 5 2 6 1 6 3 7 0 7 4 8 0 8 5 9 0 9 5 10 1 I need to split the dataset into a training and testing set based on the "Group_ID" so that 80% of the data goes into a training set and 20% into a test set. That is, I need my training set to look something like: Group_ID Item_id Target 0 1 1 0 1 1

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

问题 I am curious if there is something similar to sklearn's http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedShuffleSplit.html for apache-spark in the latest 2.0.1 release. So far I could only find https://spark.apache.org/docs/latest/mllib-statistics.html#stratified-sampling which does not seem to be a great fit for splitting heavily imbalanced dataset into train /test samples. 回答1: Let's assume we have a dataset like this: +---+-----+ | id|label| +---+-----+ |

问题 How to split data into Train and Test by using time-based split. I know that train_test_split splits it randomly how to split it based on Time. X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42) # this splits the data randomly as 67% test and 33% train How to Split the same data set based on time as 67% train and 33% test? The dataset has a column TimeStamp. I tried searching on the similar questions but was not sure about the approach. Can someone