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)sklearn train_test_split
import numpy as np
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
)train_size
You have to specify this parameter only if you’re not specifying the test_size. This is the same as test_size, but instead you tell the class what percent of the dataset you want to split as the training set.train-test split code in pandas
df_permutated = df.sample(frac=1)
train_size = 0.8
train_end = int(len(df_permutated)*train_size)
df_train = df_permutated[:train_end]
df_test = df_permutated[train_end:]Copyright © 2021 Codeinu
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