aws-samples / aws-machine-learning-university-accelerated-tab Goto Github PK

One thing we can do is to test the model with the data we used to train it, and use sklearn's metrics functions to examine the performance of the classifier.

Isn't this wrong? Shouldn't the data set be divided into training and validation sets?

After loading final project data

import pandas as pd
import numpy as np

import warnings
warnings.filterwarnings("ignore")
  
training_data = pd.read_csv('../data/final_project/training.csv')
test_data = pd.read_csv('../data/final_project/test_features.csv')
y_test = pd.read_csv('../data/final_project/y_test.csv')

print('The shape of the training dataset is:', training_data.shape)
print('The shape of the test dataset is:', test_data.shape)
print('The shape of the y_test is:', y_test.shape)

The shape of the training dataset is: (71538, 13)
The shape of the test dataset is: (23846, 12)
The shape of the y_test is: (23845, 1)

The number of samples for test features differs from y_test.
Is it correct?

Hi,
YouTube videos of this course has set Dutch automatic closed caption language, instead English. As a English second language learner, support of closed caption is valuable; this setup error prejudice either Google automatic translation to other languages.
Congratulations for the course content, it's great!

NB: I apologize to post this issue here, but I didn't find the correct channel to report this on YouTube.