"train test validation split" Code Answer's
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sklearn split train test
Pouyan
on Mar 04, 2020
import numpy as np
from sklearn.model_selection import train_test_split
X, y = np.arange(10).reshape((5, 2)), range(5)
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.33, random_state=42)
X_train
# array([[4, 5],
# [0, 1],
# [6, 7]])
y_train
# [2, 0, 3]
X_test
# array([[2, 3],
# [8, 9]])
y_test
# [1, 4]Source: stackoverflow.com
train,test,dev python
Victorious Vendace
on Jul 17, 2020
import numpy as np
import pandas as pd
def train_validate_test_split(df, train_percent=.6, validate_percent=.2, seed=None):
np.random.seed(seed)
perm = np.random.permutation(df.index)
m = len(df.index)
train_end = int(train_percent * m)
validate_end = int(validate_percent * m) + train_end
train = df.iloc[perm[:train_end]]
validate = df.iloc[perm[train_end:validate_end]]
test = df.iloc[perm[validate_end:]]
return train, validate, testSource: stackoverflow.com
train test validation split
Brainy Bug
on May 19, 2021
X_train, X_test, y_train, y_test
= train_test_split(X, y, test_size=0.2, random_state=1)
X_train, X_val, y_train, y_val
= train_test_split(X_train, y_train, test_size=0.25, random_state=1) # 0.25 x 0.8 = 0.2
Source: datascience.stackexchange.com
splitting data into training and testing sklearn
Cruel Crane
on Apr 22, 2020
train_features, test_features, train_labels, test_labels =
train_test_split(features, labels)
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