Machine Learning | Train Test Split in Cross Validation using Pandas and Numpy
Machine Learning | Train Test Split in Cross Validation using Pandas and Numpy
How to do Train Test Split using
1. sklearn.model_selection.train_test_split
2. pandas
3. numpy
4. train test split in a sequential order
'''
Method No 2 using Pandas
import pandas as pd
import numpy as np
from sklearn.datasets import make_blobs
X, y = make_blobs(n_samples=10, random_state=1)
df = pd.DataFrame(X)
df.sample(frac=0.5)
df.sample(frac=0.6)
df.sample(frac=0.8)
train train Split -- 60 : 40
np.split(df.sample(frac=1),[6])
np.split(df.sample(frac=1),[7]) # 70:30
np.split(df.sample(frac=1),[6,8])
train, test = np.split(df.sample(frac=1),[int(0.6*len(df))])
train train Split -- 70 : 30
train, test = np.split(df.sample(frac=1),[int(0.7*len(df))])
train validate test split -- 60 : 20 : 20
train,val, test = np.split(df.sample(frac=1),[int(0.6*len(df)),
int(0.8*len(df))])
train validate test split -- 50 : 30 : 20
train,val, test = np.split(df.sample(frac=1),[int(0.5*len(df)),
int(0.8*len(df))])
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