Machine Learning
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Machine Learning / Data Science
DifficultyMedium
This problem is a supervised learning problem where the goal is to predict the label of a point in 10 dimensions, given a dataset of 100,000 points with labels.
About this solution: The candidate's solution is correct and demonstrates a level of completeness in solving the problem. The candidate's approach is also general enough to be applicable to similar problems.
Machine Learning / Data Science
DifficultyMedium
The k-nearest neighbors algorithm is a supervised learning algorithm that can be used for both classification and regression. The algorithm works by finding the k nearest neighbors to a given data point, and then using those neighbors to predict the class or value of the data point.
About this solution: The candidate's solution is complete and solves the problem. The approach is generally sound, although there may be more efficient ways to implement the algorithm.
This problem asks for a machine learning model that can predict whether or not a loan will default.
About this solution: This is a good start, but the candidate's solution is incomplete. In order to make an accurate prediction, the machine learning algorithm would need to take into account a variety of factors, such as the borrower's credit score, employment history, and the amount of the loan.
Machine Learning / Data Science
DifficultyMedium
A machine learning algorithm is used to identify which features in a dataset are most predictive of the target variable. This can be used to reduce the dimensionality of the data and improve the performance of the machine learning models.
About this solution: The candidate's solution is complete and solves the problem. The candidate has correctly identified that a feature selection algorithm is needed to identify the most predictive features. This is a good approach to the problem.
A system that can automatically detect plagiarism in documents is needed.
About this solution: The candidate's solution demonstrates a level of completeness and solves the problem. The general approach is efficient and would work well in practice.
To predict the price of a house, we can use linear regression to learn the relationship between house size and price. Then, to predict the price of a new house, we can simply plug in the size of the house into the trained model.
About this solution: The candidate's solution is correct and demonstrates a good understanding of the problem. The linear regression model is a good choice for this problem.
This problem involves clustering customer reviews in order to group together similar reviews and distinguish different groups of reviews. This can be done using unsupervised learning methods such as k-means clust ering.
About this solution: The candidate's solution is complete and solves the problem. The approach is sensible and would likely work well in practice.