Introduction to Machine Learning in Sports Analytics Quiz Answers

Get All Weeks Introduction to Machine Learning in Sports Analytics Quiz Answers

In this course, students will explore supervised machine learning techniques using the python scikit learn (sklearn) toolkit and real-world athletic data to understand both machine learning algorithms and how to predict athletic outcomes.

Building on the previous courses in the specialization, students will apply methods such as support vector machines (SVM), decision trees, random forest, linear and logistic regression, and ensembles of learners to examine data from professional sports leagues such as the NHL and MLB as well as wearable devices such as the Apple Watch and inertial measurement units (IMUs).

By the end of the course students will have a broad understanding of how classification and regression techniques can be used to enable sports analytics across athletic activities and events.

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Week 1: Introduction to Machine Learning in Sports Analytics Quiz Answers

Quiz 1: Assignment 1

Q1. There are a few main branches of machine learning. When you have the label for your training data and you want to build a model which predicts for that label, what kind of machine learning is that?

  • Supervised
  • Reinforcement
  • Artificial
  • Unsupervised

Q2. What is a minority class of data?

  • Labels which are poorly chosen
  • Labels which are easy to predict
  • Labels about demographics of players
  • Labels you have fewer instances for

Q3. Which data do you make available to the machine learning algorithm to learn from?

  • Training data
  • Validation data
  • Evaluation data
  • Testing data

Q4. In my model of the NHL game data I had to deal with the introduction of a new team, the Vegas Golden Knights. For this team I just naively decided to fill the historical stats with just mean values from the other teams. But assume that I took a different strategy, and dropped all games where the Vega Gold Knights played. What is the new metric of accuracy for my model after dropping Gold Knights games from the data?

For this question, don’t change the training set size, and the testing set size will shrink automatically. Put your answer in to two decimal places.

Enter answer here

Week 2: Introduction to Machine Learning in Sports Analytics Quiz Answers

Quiz 1: Assignment 2

Q1. In a two class linear SVM, what is the street?

  • A random walk of the support vectors
  • A polynomial equation which best represents the classes
  • The two features which create our SVM
  • The hyperplane which separates two classes

Q2. Which function do you call in order to build a model from data in sklearn?

  • model()
  • train()
  • build()
  • fit()

Q3. What is the purpose of cross validation?

  • To balance data as we get more classes (labels) to predict
  • To get a better estimate as to the accuracy of the final model
  • To build a more accurate model
  • To build a confusion matrix

Q4. Taking a look at the baseball data where we made a multiclass prediction, create a confusion matrix and study it. Which class do we regularly over-predict the most? Provide the label of this class as two capitalized characters (e.g. AB).

Enter answer here

Q5. Will this class have a higher precision or recall score?

  • recall
  • preceision

Week 3: Introduction to Machine Learning in Sports Analytics Quiz Answers

Quiz 1: Assignment 3

Q1. What does it mean for a set of observations to be “pure”?

  • It’s imbalanced with respect to class
  • It’s balanced with respect to class
  • It’s about a Canadian team or player
  • It’s homogenous with respect to class

Q2. For each split, how many features does CART split on at once?

  • 1
  • Any number
  • All
  • 0

Q3. What kind of prediction target does an M5P tree make?

  • A label
  • A numeric value
  • An array

Q4. After a descision tree splits on a feature, will it split again on that feature in a subtree?

  • No
  • Maybe
  • Yes

Q5. Go back to our NHL game outcome prediction task in observations.csv. Apply a CART DecisionTree to this problem with GridSearchCV over the following parameter space:


Set your cv=10, use accuracy as your metric, and drop the Vegas Golden Knights. Set your training set to be observations[0:800] and your validation set to observations[800:], and use my favorite number for the randomization state. What level of accuracy does your model produce (to four decimal places)?

Enter answer here

Q6. Which set of parameters are the best in the previous model? Input your parameters as a string value of the max_depth:min_samples_leaf, e.g. 5:20 if GridSearchCV found a max_depth=5 and min_samples_leaf=20 the correct answer.

Enter answer here

Week 4: Introduction to Machine Learning in Sports Analytics Quiz Answers

Quiz 1: Assignment 4

Q1. If you were making a classifier using two features and you visualized your data and saw it was separated by roughly a 45 degree, which classifier would you start with first for best results?

  • SVM
  • Confusion Matrix
  • M5P Tree
  • Descision Tree

Q2. What is the purpose of GridSearch?

  • It is a regression mechanism using descision trees.
  • It improves our understanding of the confusion matrix.
  • It helps to prune leaves from large trees.
  • It provides a hyperparameter tuning method.

Q3. Which kind of ensemble method creates multiple classifiers for you with random subsets of data?

  • Boosting
  • Bagging
  • Voting
  • Stacking

Q4. Which kind of modelers can be ensembled together into a voting ensemble for the boxing punch detection problem (choose all that apply)?

Note that the boxing punch detection problem is a classification task.

  • Decision Trees
  • SVMs
  • Polynomial SVMs
  • Linear Regression
  • Logistic Regression
  • Cross Validation
  • Bagging Classifier
  • Gradient Boosting Classifier
  • Packaging Classifier

I hope this Introduction to Machine Learning in Sports Analytics Coursera Quiz Answers would be useful for you to learn something new from the Course. If it helped you, don’t forget to bookmark our site for more Quiz Answers.

This course is intended for audiences of all experiences who are interested in learning about new skills in a business context; there are no prerequisite courses.

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