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)
code for test and train split
from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.33, random_state=42)
Source: scikit-learn.org
pandas split train test
from sklearn.model_selection import train_test_split train, test = train_test_split(df, test_size=0.2)
Source: stackoverflow.com
train test split python
from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)
train test split
import numpy as np from sklearn.model_selection import train_test_split # Data example X, y = np.arange(10).reshape((5, 2)), range(5) # Split data into train and test sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
Source: scikit-learn.org
train test split
from sklearn.model_selection import train_test_split # Split into training and test set X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=42, stratify=y)
Source: campus.datacamp.com