Data Analysis with Python
Week 5 Quiz Answer
Practice Quiz 1
Model Evaluation
Q1) What is the correct use of the "train_test_split function such that 90% of the data samples will be utilized for training, the parameter "random_state" is set to zero, and the input variables for the features and targets are x_data, y data respectively.
- train_test_split(x_data, y_data, test_size-0.9, random_state)
- train_test_spilt(x_data, y_data, test_size-0.1, random_state)
Q2) What is the problem with the In-sample evaluation
- it does not tell us how well the trained model can be used to predict new data
- it's slow because there is more data
Practice Quiz 2
Overfitting, Underfitting and Model Selection
Q1) what model should you select
- a
- b
- c
Q2) the following is an example of:
- overfitting
- perfect fit
- underfitting
Practice Quiz 3
Ridge Regression
Q1) consider the three polynomial regression models, select the model
that would most benefit from Ridge Regression:
- a
- b
- c
Q2) the following models were all trained on the same data, select the model
with the highest value for alpha:
- a
- b
- c
Practice Quiz 4
Quiz: Model Refinement
Q1) What is the correct use of the "train_test_split" function such
that 40% of the data samples will be utilized for testing, the parameter
"random state" is set to zero, and the input variables for the features
and X_data, y data respectively?
- train_test_split(x_data, y data, test_size=0, random_state=0.4)
- train_test_split(x_data, y_data, test_size=0.4, random_state=0)
- train_test_split(x_data, y_data)
Q2) What is the output of the following code?
Cross_val_score(lre, x_data, y_data, cv=2)
- This function finds the free parameter alpha
- The average R^2 on the test data for each of the two folds
- The predicted values of the test data using cross-validation
Q3) What is the code to create a ridge regression object RR with an
alpha term equal to 10?
- RRLinearRegression(alpha=10)
- RR=Ridge(alpha=10)
- RR=Ridge(alpha=1)
Q4) What dictionary value would we use to perform a grid search for the
following values of alpha? 1.10. 100.NO other parameter values should be
tested
- alpha=[1, 10, 1001]
- [{'alpha': [1,10,100]}]
- [{'alpha': [0.001,0.1,1, 10, 100, 1000, 10000, 100000, 100000],'normalitze':[True,false]}]
Q5) You have a linear model: the average R^2 value on your training
data is 0.5. you perform a 100th order polynomial transform on your data
then use these values to train another model. After this step, your
average R^2 is 0.99: Which of the following comments is correct?
- You should always use the simplest model
- 100-th order polynomial will work better on unseen data
- The results on your training data is not the best indicator of how your model performs: you should use your test data to get a better idea
Q6) Consider the following diagram of 4 fold cross-validation From the
diagram how many folds are used for training?
- 3
- 4
- 1
Q7) The following is an example of what?
- Overfitting
- Perfect fit
- Underfitting
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Data Analysis with Python
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