Breaking News: Grepper is joining You.com. Read the official announcement!

scikit learn train test split

Add Answer

Aggressive Anaconda answered on July 15, 2020 Popularity 10/10 Helpfulness 4/10

answer scikit learn train test split

related train test split sklearn

related sklearn train_test_split

related Splitting training and test data using sklearn

related train_test_split sklearn

related train test split sklearn

related sklearn train test split

related train dev test split sklearn

scikit learn train test split

Comment

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)

Popularity 10/10 Helpfulness 4/10 Language python

Source: scikit-learn.org

Tags: python scikit-learn

Link to this answer
Share Copy Link

Contributed on Dec 01 2020

Aggressive Anaconda

0 Answers Avg Quality 2/10

Closely Related Answers

train test split sklearn

Comment

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)

Popularity 10/10 Helpfulness 10/10 Language python

Source: Grepper

Tags: python py

Link to this answer
Share Copy Link

Contributed on Nov 08 2020

The Frenchy

0 Answers Avg Quality 2/10

sklearn train_test_split

Comment

 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
)

Popularity 10/10 Helpfulness 5/10 Language python

Source: Grepper

Tags: python py

Link to this answer
Share Copy Link

Contributed on Nov 26 2020

vcwild

0 Answers Avg Quality 2/10

Splitting training and test data using sklearn

Comment

#Let us now split the dataset into train & test
from sklearn.model_selection import train_test_split
x_train,x_test, y_train, y_test = train_test_split(X, y, test_size = 0.30, random_state=0)
print("x_train ",x_train.shape)
print("x_test ",x_test.shape)
print("y_train ",y_train.shape)
print("y_test ",y_test.shape)

Popularity 9/10 Helpfulness 5/10 Language python

Source: colab.research.google.com

Tags: python py

Link to this answer
Share Copy Link

Contributed on Mar 17 2022

Helpful Hare

0 Answers Avg Quality 2/10

train_test_split sklearn

Comment

from sklearn.model_selection import train_test_split
X = df.drop("target", axis=1)
y = df["target"]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)

Popularity 10/10 Helpfulness 4/10 Language python

Source: scikit-learn.org

Tags: python py

Link to this answer
Share Copy Link

Contributed on May 20 2022

Bad Booby

0 Answers Avg Quality 2/10

train test split sklearn

Comment

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)
print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)

Popularity 10/10 Helpfulness 4/10 Language python

Source: Grepper

Tags: python py

Link to this answer
Share Copy Link

Contributed on Jul 15 2020

Clear Chipmunk

0 Answers Avg Quality 2/10

train test split sklearn

Comment

import pandas as pd
from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split

cal_housing = fetch_california_housing()
X = pd.DataFrame(cal_housing.data, columns=cal_housing.feature_names)
y = cal_housing.target

y -= y.mean()

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=0)

Popularity 9/10 Helpfulness 4/10 Language python

Source: scikit-learn.org

Tags: python py

Link to this answer
Share Copy Link

Contributed on Aug 06 2022

Odd Opossum

0 Answers Avg Quality 2/10

sklearn train test split

Comment

##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

Popularity 10/10 Helpfulness 4/10 Language python

Source: Grepper

Tags: python py

Link to this answer
Share Copy Link

Contributed on Mar 09 2022

DON-PECH

0 Answers Avg Quality 2/10

train dev test split sklearn

Comment

train, validate, test = np.split(df.sample(frac=1), [int(.6*len(df)), int(.8*len(df))])

Popularity 10/10 Helpfulness 3/10 Language python

Source: datascience.stackexchange.com

Tags: python py

Link to this answer
Share Copy Link

Contributed on Aug 17 2020

Jittery Jellyfish

0 Answers Avg Quality 2/10

scikit learn train test split

Contents

More Related Answers

scikit learn train test split

Closely Related Answers

train test split sklearn

sklearn train_test_split

Splitting training and test data using sklearn

train_test_split sklearn

train test split sklearn

train test split sklearn

sklearn train test split

train dev test split sklearn

Grepper

Documentation

Social

Legal

Contact

Oops, You will need to install Grepper and log-in to perform this action.