Search
 
SCRIPT & CODE EXAMPLE
 

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)
Comment

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
)
Comment

train_test_split sklearn

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)
Comment

train test split sklearn

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)
Comment

Splitting training and test data using sklearn

#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)
Comment

scikit learn train test 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)
Comment

train dev test split sklearn

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

train_test_split from sklearn.selection

import sklearn.model_selection as model_selectionX_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, train_size=0.65,test_size=0.35, random_state=101)print ("X_train: ", X_train)print ("y_train: ", y_train)print("X_test: ", X_test)print ("y_test: ", y_test)
Comment

sklearn train test split

##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

Comment

train test split sklearn

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)
Comment

PREVIOUS NEXT
Code Example
Python :: dataframe KeyError: 
Python :: pygame window at center 
Python :: python collections Counter sort by key 
Python :: load json py 
Python :: counter python 
Python :: how to cout in python 
Python :: how to convert binary to text in python 
Python :: python sort class by attribute 
Python :: python return specific elements from list 
Python :: python save image to pdf 
Python :: sending whatsapp message using python 
Python :: version python 
Python :: Origin in CORS_ORIGIN_WHITELIST is missing scheme or netloc 
Python :: python remove space from end of string 
Python :: clean nas from column pandas 
Python :: pandas groupby mean 
Python :: pandas read csv skip rows 
Python :: string to binary python 
Python :: pathlib path get filename with extension 
Python :: solve ax=b python 
Python :: pyspark rdd filter 
Python :: 3 dimensional array in numpy 
Python :: number system conversion python 
Python :: check if there are duplicates in list 
Python :: pd.dataframe initial columns 
Python :: plotly graph object colorscale 
Python :: takes 1 positional argument but 2 were given python 
Python :: fcm_django 
Python :: append python 
Python :: python list fill nan 
ADD CONTENT
Topic
Content
Source link
Name
9+5 =