Machine Learning Online Test

Implement the desired_marketing_expenditure function, which returns the required amount of money that needs to be invested in a new marketing campaign to sell the desired number of units.

Use the data from previous marketing campaigns to evaluate how the number of units sold grows linearly as the amount of money invested increases.

For example, for the desired number of 60,000 units sold and previous campaign data from the table below, the function should return the float 250,000.

Previous campaigns

The marketing team of the telecommunications company launched an SMS campaign to encourage customers to make more countrywide calls. The manager of the team asked you to analyze the results of the campaign.  Your main goal is to predict which customers are most likely to increase their number of calls as a result of similar SMS campaigns in the future. You have created a Random Forest model but it has problems with fitting the actual training dataset.

Which of the following steps can be taken to reduce underfitting and improve the model’s performance?

As a part of an application for iris enthusiasts, implement the train_and_predict function which should be able to classify three types of irises based on four features.

The train_and_predict function accepts three parameters:

• train_input_features - a two-dimensional NumPy array where each element is an array that contains: sepal length, sepal width, petal length, and petal width.
• train_outputs - a one-dimensional NumPy array where each element is a number representing the species of iris which is described in the same row of train_input_features. 0 represents Iris setosa, 1 represents Iris versicolor, and 2 represents Iris virginica.
• prediction_features - two-dimensional NumPy array where each element is an array that contains: sepal length, sepal width, petal length, and petal width.

The function should train a classifier using train_input_features as input data and train_outputs as the expected result. After that, the function should use the trained classifier to predict labels for prediction_features and return them as an iterable (like list or numpy.ndarray). The nth position in the result should be the classification of the nth row of the prediction_features parameter.

A SAAS company reported a 20% churn rate (the rate at which customers stop subscribing to a service ) in the last quarter, which is much larger than the expected 12%. Your manager asked you to create a model that can predict the churners of the next 12 months in advance. With the help of this model, the marketing team will launch churn prevention campaigns to reduce the churn rate.

You decided to use a Gradient Boosting model. The parameters used in the model are the following:

• Learning rate = 0.3
• Maximum depth of a tree = 6
• Minimum sum of instance weight needed in a child = 1
• Subsample = 1
• L1 regularization term on weights = 0
• L2 regularization term on weights = 1

Currently the model has a problem, the training set has more than 95% accuracy but the test set has only 60% accuracy.

Which of the following steps can help solve the issue?

A classifier that predicts if an image contains only a cat, a dog, or a llama produced the following confusion matrix:

What is the accuracy of the model, in percentages?