Machine Learning (DTSC 3220)

Explanation:

Deep learning is a specialized subset of machine learning that relies on neural networks with multiple layers to automatically learn hierarchical feature representations from data. Neural networks themselves are a class of machine learning models inspired by biological neural systems, and they can be shallow (few layers) or deep (many layers). Therefore, the correct relationship is that deep learning is a subset of neural networks, which in turn are a subset of the broader field of machine learning.

Correct Answer:

Deep learning is a subset of neural networks, which are a subset of machine learning.

Why Other Options Are Wrong:

Deep learning is a more advanced version of machine learning, while neural networks are used exclusively for image processing

This is incorrect because neural networks are not limited to image processing; they can be applied to text, audio, and tabular data as well. Deep learning is not simply a “more advanced version” of machine learning but a subset defined by neural network architectures.

Machine learning and deep learning are terms used interchangeably to describe the same concept

This is incorrect because deep learning is a subset of machine learning, not synonymous with it. Machine learning encompasses a wider range of models, including decision trees, support vector machines, and logistic regression, beyond deep neural networks.

Neural networks are a branch of mathematics unrelated to machine learning or deep learning

This is incorrect because neural networks are a core component of machine learning and deep learning. They are mathematical models used for predictive modeling and pattern recognition, directly tied to these fields.

Explanation:

Data quality issues, such as missing values, errors, or inconsistencies, can significantly affect predictive modeling outcomes. Poor-quality data can introduce biases that skew the model’s learning process, resulting in inaccurate predictions when applied to new or unseen data. This is because the model may learn patterns that reflect errors or anomalies rather than the true underlying relationships in the data. Ensuring high-quality data is critical for building reliable, accurate predictive models.

Correct Answer:

They may introduce biases and reduce the accuracy of predictions.

Why Other Options Are Wrong:

They can enhance the model's ability to generalize to new data.

This is incorrect because data quality issues do not improve generalization. In fact, errors or inconsistencies in the dataset usually reduce the model’s ability to generalize, as the model may learn spurious patterns or noise rather than meaningful trends. High-quality, representative data is required to enhance generalization.

They have no significant effect on the performance of the model.

This is incorrect because data quality directly impacts model performance. Poor data can lead to reduced accuracy, biased predictions, or unreliable outcomes. Assuming no effect ignores the critical role that data integrity plays in predictive modeling.

They only affect the computational efficiency of the model.

This is incorrect because while some data issues might slightly affect computational performance, the primary impact of poor data quality is on prediction accuracy and model reliability, not just processing speed.

Explanation:

In gradient descent optimization, a stopping criterion determines when the iterative process of updating model parameters should be terminated. Stopping criteria can include reaching a predefined number of iterations, achieving a sufficiently small change in the loss function between successive iterations, or when the gradient magnitude falls below a threshold. Using stopping criteria ensures that the algorithm halts at an appropriate point, preventing unnecessary computation and avoiding potential overfitting or oscillations around a minimum.

Correct Answer:

It specifies when to terminate the optimization process

Why Other Options Are Wrong:

It defines the maximum learning rate for the algorithm

This is incorrect because the learning rate is a hyperparameter that controls the step size during updates. The stopping criterion does not set or limit the learning rate; it only determines when to stop iterating.

It determines the number of features to include in the model

This is incorrect because feature selection is unrelated to the stopping criterion. The stopping criterion affects the optimization process, not which features are used in the model.

It evaluates the performance of the model on the training data

This is incorrect because evaluation of model performance is a separate step in model assessment. The stopping criterion simply signals when to halt parameter updates, not how well the model performs.

Explanation:

The primary purpose of Principal Component Analysis (PCA) is to reduce the number of features in a dataset while preserving as much variance as possible. PCA transforms the original correlated features into a smaller set of uncorrelated principal components, which capture the majority of the information in the data. This reduces computational complexity, mitigates multicollinearity, and aids in visualization of high-dimensional datasets, making patterns easier to identify and interpret.

Correct Answer:

To reduce the number of features while preserving variance

Why Other Options Are Wrong:

To increase the dimensionality of the dataset

This is incorrect because PCA reduces, not increases, the number of dimensions in a dataset. The goal is simplification while retaining important information.

To classify data into predefined categories

This is incorrect because PCA is an unsupervised dimensionality reduction technique. It does not perform classification; it transforms features to capture variance.

To visualize data in its original high-dimensional space

This is incorrect because visualizing data in high dimensions is often impractical. PCA facilitates visualization by projecting data into a lower-dimensional space.

Explanation:

Supervised learning problems are considered classification problems when the target variable is categorical. In classification, the model predicts discrete labels, such as disease presence/absence, fraud detection, or patient risk categories. In contrast, regression problems involve continuous numerical target variables. The type of target variable—categorical versus numerical—determines whether the supervised learning task is classification or regression.

Correct Answer:

The target variable is a categorical value

Why Other Options Are Wrong:

The target variable is a numerical value

This is incorrect because numerical target variables indicate a regression problem, not classification.

The target variable is unknown

This is incorrect because unknown targets characterize unsupervised learning, not supervised learning. Classification still requires known labels for training.

The target variable is known but there are not enough samples to draw a unique conclusion from

This is incorrect because the number of samples affects model reliability and generalization, but it does not change the problem type. Classification is defined by the categorical nature of the target variable, regardless of sample size.

Explanation:

In predictive modeling, the target variable is the predefined attribute whose value the model aims to predict. It is the outcome of interest, and the model uses input features to estimate or classify this variable. For example, in a medical dataset, the target variable might be the presence or absence of a disease, while other features like age, blood pressure, and lab results serve as inputs. Identifying the target variable is essential for supervised learning, as it guides the model in learning the relationship between inputs and outputs.

Correct Answer:

The predefined attribute whose value is being predicted in a predictive model.

Why Other Options Are Wrong:

The representation of a data point described by a set of attributes within a predictive model's dataset

This is incorrect because this describes a data instance or feature vector, not the target variable. The target variable is specifically the output being predicted.

A variable that describes a characteristic of an instance within a predictive model

This is incorrect because this describes a feature or input variable, not the target variable. Features are used to predict the target.

A model used to study and find relationships within data

This is incorrect because the target variable is not a model itself; it is the outcome the model predicts. The model uses features to learn and estimate the target variable.

Explanation:

The primary advantage of using a mini-batch in gradient descent is that it allows for faster convergence by updating the model weights more frequently. Instead of computing the gradient over the entire dataset (as in batch gradient descent), mini-batch gradient descent computes the gradient on smaller subsets of data, which enables the model to make updates more often. This frequent updating can lead to quicker convergence and can help the model escape shallow local minima, improving training efficiency and performance.

Correct Answer:

It allows for faster convergence by updating weights more frequently.

Why Other Options Are Wrong:

It eliminates the need for a learning rate

This is incorrect because mini-batch gradient descent still requires a learning rate to determine the size of each weight update. The learning rate is an essential hyperparameter that controls convergence speed and stability.

It guarantees finding the global minimum

This is incorrect because mini-batch gradient descent does not guarantee reaching the global minimum. The algorithm may converge to local minima or saddle points, depending on the loss surface and learning rate.

It requires less memory than storing the entire dataset

This is incorrect because the main memory advantage comes from not needing to load the entire dataset at once, but the key advantage emphasized in the context of convergence is the more frequent weight updates, not just memory efficiency.

Explanation:

In supervised machine learning, the class label attribute refers to the target variable that the predictive model is designed to predict. It represents the outcome of interest, such as disease presence, customer churn, or a categorical classification. The model uses input features to learn patterns that allow it to accurately predict this label on new, unseen data. Understanding the class label is essential for selecting the correct algorithm and evaluating model performance.

Correct Answer:

The label the predictive algorithm is aiming to predict

Why Other Options Are Wrong:

The label that is most informative for the algorithm to use for prediction

This is incorrect because while informative features help prediction, the class label itself is the target to be predicted, not simply an informative feature. The model uses features to predict the class label, not the other way around.

The majority case indicates the most common label in the data set

This is incorrect because the majority case refers to the most frequent occurrence in the dataset, which may not necessarily represent the class label conceptually. The class label is the target of prediction, independent of frequency.

The minority case that indicates non-independent variables, and not useful for prediction

This is incorrect because the class label is not defined by minority cases or lack of independence. It is the primary target that the model seeks to predict, regardless of its frequency in the dataset.

Explanation:

Evaluation of a machine learning model provides critical feedback on how well the model performs on unseen data, identifying strengths and weaknesses in its predictions. Through evaluation metrics such as accuracy, precision, recall, and F1-score, developers gain insights into specific areas where the model may underperform, such as certain classes or types of errors. These insights guide model improvements, feature engineering, or further training, ensuring the model is more robust and effective when deployed.

Correct Answer:

By providing insights into the model's performance and areas for improvement

Why Other Options Are Wrong:

By reducing the amount of data needed for training

This is incorrect because evaluation does not affect the quantity of training data required. Its role is to assess model performance, not change dataset size.

By identifying the most significant features for model prediction

This is incorrect because feature importance is determined through specific techniques such as feature selection or permutation importance, not through general model evaluation. Evaluation assesses performance, not feature significance directly.

By automatically tuning the model's hyperparameters without human intervention

This is incorrect because hyperparameter tuning is a separate process that may be automated using methods like grid search or Bayesian optimization. Evaluation informs this process but does not perform automatic tuning by itself.