Final Exam

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What is one-hot encoding?

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One-hot encoding is a method of converting categorical variables into a binary format, where each category is represented as a binary vector. For example, if a variable has three categories (Red, Green, Blue), it would be represented as: Red = [1, 0, 0], Green = [0, 1, 0], Blue = [0, 0, 1].

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What is clustering in data analysis?

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Clustering is an unsupervised learning task that involves grouping a set of objects in such a way that objects in the same group (or cluster) are more similar to each other than to those in other groups. It is commonly used for exploratory data analysis.

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What is the mode in statistics?

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The mode is the value that appears most frequently in a data set. For example, in the data set {1, 2, 2, 3}, the mode is 2.

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What is a multilayer perceptron (MLP)?

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A multilayer perceptron is a type of artificial neural network that consists of multiple layers of nodes (neurons), where each layer is fully connected to the next one. MLPs are used for supervised learning tasks.

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What is the purpose of data preprocessing?

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Data preprocessing is the process of cleaning and transforming raw data into a format suitable for analysis. It includes steps like handling missing values, normalizing data, and encoding categorical variables.

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What is K-means clustering?

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K-means clustering is a popular unsupervised learning algorithm that partitions data into K distinct clusters based on feature similarity. The algorithm iteratively assigns data points to the nearest cluster centroid and updates the centroids.

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What is a classification task in machine learning?

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A classification task is a supervised learning problem where the goal is to predict the categorical label of new observations based on past observations. Examples include spam detection and image recognition.

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NGSS.MS-ETS1-4

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What is the significance of the training and testing phases in machine learning?

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The training phase involves using a labeled dataset to train a model, while the testing phase evaluates the model's performance on unseen data. This helps assess the model's generalization ability.

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What is feature engineering?

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Feature engineering is the process of using domain knowledge to create new input features from existing ones, improving the performance of machine learning models.

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What is the difference between supervised and unsupervised learning?

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Supervised learning uses labeled data to train models, while unsupervised learning works with unlabeled data to find patterns or groupings.