Model Training
Presentation
•
Information Technology (IT)
•
University
•
Practice Problem
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Hard
Thomas School
Used 2+ times
FREE Resource
11 Slides • 22 Questions
1
2
3
4
Multiple Choice
What's the first step of model training?
Loading the data
5
6
Drag and Drop
7
Drag and Drop
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9
Multiple Choice
What's the purpose of data exploration?
To understand the data's structure, patterns and problems.
10
11
Multiple Choice
Why do we need to encode categorical data?
We need to encode categorical data becayse machine learning needs numbers.
12
Multiple Choice
Which of the following columns in the houses dataset are categorical variables?
bedrooms, area_m2
district, apt_floor
year_built, total_floors
price, bathrooms
13
Multiple Choice
Which step in the model training process involves converting categorical variables into numerical format?
Load the data
Data exploration
Data encoding
Train the model
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15
Multiple Choice
What do we import from sklearn to divide our data?
16
Fill in the Blanks
17
Multiple Choice
What's in variable y?
Prediction
Target
Features
18
Multiple Choice
What's in variable X?
Prediction
Target
Features
19
Multiple Select
What doest test_size = 0.2 mean?
test_size = 0.2 means that 80% of the data is for training purposes.
test_size = 0.2 means 20% of the data is for testing.
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21
Multiple Choice
How to train the model?
model.train()
model.fit()
model.predict()
model.AI()
22
Multiple Choice
Which variables do we train the model on?
X_test, y_test
X_train, y_train
X, y
Train, test
23
Multiple Choice
After training the model, which step should be performed next according to the model training steps?
Make predictions
Evaluate the model
Load the data
Data encoding
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25
Multiple Choice
How do we make predictions?
model.predict()
model.pred()
model.fit()
model.answer()
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Multiple Choice
Which variable do we use for prediction?
y_train
y_test
X_train
X_test
27
Multiple Choice
Why do we use X_test for predictions?
We use X_test for predictions to see model performance on new, unseen data.
28
Multiple Choice
Which Python function is used to make predictions on the test set in a machine learning model?
model.fit(X_test)
model.evaluate(X_test)
model.predict(X_test)
model.score(X_test)
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It means: On average, our prediction is wrong by 191万.
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Multiple Choice
Why do we use both y_test and y_pred when evaluating the model?
We use y_test to compare the model's predictions (y_pred) to real data.
31
Multiple Choice
The bigger MAE the better the model.
True
False
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Multiple Select
What's the meaning of this read line?
predicted price = actual price
x = y
the model is good
the model is bad
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