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

You are developing a recommendation system for a book platform. Below is the video showing the actions of a new user interacting with the site: Based on user behavior, which type of recommendation filtering method would be the most appropriate to use?

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.

Your team has decided to use a k-nearest neighbors model for fraud detection model, but nobody has tuned the hyperparameters yet. You have been asked to implement tune_knn to perform an exhaustive search over a given list of neighbor counts and return the best value.

Use accuracy as the scoring metric. If multiple values achieve the same accuracy, return the first value that achieves the best score.

For example, the following code:

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
X, y = make_classification(
    n_samples=100,
    n_features=8,
    n_informative=4,
    random_state=42
)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
print(tune_knn(X_train.tolist(), y_train.tolist(), X_test.tolist(), y_test.tolist(), [11, 1, 6]))

Should print:

6

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?

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?

The following .csv file contains the data from a classifier model that predicts if an image contains a dog: predictions.csv 

The first column contains information if the dog is in the image or not. The second column contains the classifier prediction, which is in the interval 0-100, with higher values meaning that the classifier is more confident that image contains a dog.

What is the value of the decision boundary that will maximize the accuracy of the model? Values greater than or equal to the decision boundary will be treated as positive.