Answers for "lb = LabelBinarizer() #split data into training and test set X_train, X_test, y_train, y_test = train_test_split(image_list, label_list, test_size=0.1, random_state=42)"

Python

train test split sklearn

from sklearn.model_selection import train_test_split

X = df.drop(['target'],axis=1).values   # independant features
y = df['target'].values					# dependant variable

# Choose your test size to split between training and testing sets:
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)

Posted by: Guest on November-08-2020

pandas split train test

from sklearn.model_selection import train_test_split


y = df.pop('output')
X = df

X_train,X_test,y_train,y_test = train_test_split(X.index,y,test_size=0.2)
X.iloc[X_train] # return dataframe train

Posted by: Guest on December-24-2020

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Answers for "lb = LabelBinarizer() #split data into training and test set X_train, X_test, y_train, y_test = train_test_split(image_list, label_list, test_size=0.1, random_state=42)"

Code answers related to "lb = LabelBinarizer() #split data into training and test set X_train, X_test, y_train, y_test = train_test_split(image_list, label_list, test_size=0.1, random_state=42)"

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