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44 lines
1.5 KiB
44 lines
1.5 KiB
2 years ago
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# Exercise 2 Linear regression in 1D
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The goal of this exercise is to understand how the linear regression works in one dimension. To do so, we will generate a data in one dimension. Using `make regression` from Scikit-learn, generate a data set with 100 observations:
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```python
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X, y, coef = make_regression(n_samples=100,
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n_features=1,
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n_informative=1,
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noise=10,
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coef=True,
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random_state=0,
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bias=100.0)
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```
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1. Plot the data using matplotlib. The plot should look like this:
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![alt text][q1]
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[q1]: ./w2_day1_ex2_q1.png "Scatter plot"
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2. Fit a LinearRegression from Scikit-learn on the generated data and give the equation of the fitted line. The expected output is: `y = coef * x + intercept`
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3. Add the fitted line to the plot. the plot should look like this:
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![alt text][q3]
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[q3]: ./w2_day1_ex2_q3.png "Scatter plot + fitted line"
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4. Predict on X
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5. Create a function that computes the Mean Squared Error (MSE) and compute the MSE on the data set. *The MSE is frequently used as well as other regression metrics that will be studied later this week.*
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```
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def compute_mse(y_true, y_pred):
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#TODO
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return mse
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```
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Change the `noise` parameter of `make_regression` to 50
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6. Repeat question 2, 4 and compute the MSE on the new data.
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https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html
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