First assignment done for the University of Washington’s Machine Learning Foundations course in regression analysis. There were 9 questions to answer having done the slides and practicals for week 1. An interesting way to pass this assignment – one has until the 9th October to get above 80% – so 8 out of 9 required. One can do the assignment at most 3 times in every 8 hour period. Anyway I got the following 9 questions correct on the first attempt

1 2 3 |
Passed 9/9 points earned (100%) Quiz passed! |

Q1. Which figure represents an overfitted model?

Q2. True or false: The model that best minimizes training error is the one that will perform best for the task of prediction on new data.

Q3. The following table illustrates the results of evaluating 4 models with different parameter choices on some data set. Which of the following models fits this data the best?

Model index | Parameters (intercept, slope) | Residual sum of squares (RSS) |

1 | (0,1.4) | 20.51 |

2 | (3.1,1.4) | 15.23 |

3 | (2.7, 1.9) | 13.67 |

4 | (0, 2.3) | 18.99 |

Q4. Assume we fit the following quadratic function: f(x) = w0+w1*x+w2*(x^2) to the dataset shown (blue circles). The fitted function is shown by the green curve in the picture below. Out of the 3 parameters of the fitted function (w0, w1, w2), which ones are estimated to be 0? *(Note: you must select all parameters estimated as 0 to get the question correct.)*

Q5. Assume we fit the following quadratic function: f(x) = w0+w1*x+w2*(x^2) to the dataset shown (blue circles). The fitted function is shown by the green curve in the picture below. Out of the 3 parameters of the fitted function (w0, w1, w2), which ones are estimated to be 0? *(Note: you must select all parameters estimated as 0 to get the question correct.)*

Q6. Assume we fit the following quadratic function: f(x) = w0+w1*x+w2*(x^2) to the dataset shown (blue circles). The fitted function is shown by the green curve in the picture below. Out of the 3 parameters of the fitted function (w0, w1, w2), which ones are estimated to be 0? *(Note: you must select all parameters estimated as 0 to get the question correct.)*

Q7. Assume we fit the following quadratic function: f(x) = w0+w1*x+w2*(x^2) to the dataset shown (blue circles). The fitted function is shown by the green curve in the picture below. Out of the 3 parameters of the fitted function (w0, w1, w2), which ones are estimated to be 0? *(Note: you must select all parameters estimated as 0 to get the question correct.)*

Q8. Would you ** not** expect to see this polot as a plot of training and test error curves?

Q9. True or false: One always prefers to use a model with more features since it better captures the true underlying process.