code for test and train 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, random_state=42)code for test and train 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, random_state=42)sklearn split train test
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]how to split train and test data in r
library(caTools)
sample <- sample.split(data$y, SplitRatio = 0.7)
train_data <- subset(data, sample == TRUE)
test_data <- subset(data, sample == FALSE)svd movielens data train and test
from surprise import Dataset, Reader, SVD, accuracy
from surprise.model_selection import train_test_split
# instantiate a reader and read in our rating data
reader = Reader(rating_scale=(1, 5))
data = Dataset.load_from_df(ratings_f[['userId','movieId','rating']], reader)
# train SVD on 75% of known rates
trainset, testset = train_test_split(data, test_size=.25)
algorithm = SVD()
algorithm.fit(trainset)
predictions = algorithm.test(testset)
# check the accuracy using Root Mean Square Error
accuracy.rmse(predictions)
RMSE: 0.7724
# check the preferences of a particular user
user_id = 7010
predicted_ratings = pred_user_rating(user_id)
pdf = pd.DataFrame(predicted_ratings, columns = ['movies','ratings'])
pdf.sort_values('ratings', ascending=False, inplace=True)
pdf.set_index('movies', inplace=True)
pdf.head(10)Copyright © 2021 Codeinu
Forgot your account's password or having trouble logging into your Account? Don't worry, we'll help you to get back your account. Enter your email address and we'll send you a recovery link to reset your password. If you are experiencing problems resetting your password contact us
