“Sklearn -Zug -Test geteilt” Code-Antworten

Zugtest 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)
The Frenchy

Code für Test- und Zugaufteilung

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)
Cute Chimpanzee

Zugtest geteilte 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)
JJSSEECC

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
)
vcwild

Zugtest geteilte Python

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)

Gabriel Juri

Sklearn -Zug -Test geteilt

##sklearn train test split

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)

#OR Randomly split your whole dataset to your desired percentage, insted of using a  ttarget variable:

training_data = df.sample(frac=0.8, random_state=25) #here we choose 80% as our training sample and for reproduciblity, we use random_state of 42
testing_data = df.drop(training_data.index) # testing sample is 20% of our initial data

DON-PECH

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