________ regression is best suited for binary classification problems.
- Lasso
- Linear
- Logistic
- Polynomial
Logistic regression is a type of regression used in binary classification problems, where the outcome variable has two possible classes (e.g., yes/no, true/false, 0/1). It models the probability of one of the classes.
A key challenge in machine learning ethics is ensuring that algorithms do not perpetuate or amplify existing ________.
- Inequalities
- Biases
- Advantages
- Opportunities
Ensuring that algorithms do not perpetuate or amplify existing inequalities is a fundamental challenge in machine learning ethics. Addressing this challenge requires creating more equitable models and datasets.
A real estate company wants to predict the selling price of houses based on features like square footage, number of bedrooms, and location. Which regression technique would be most appropriate?
- Decision Tree Regression
- Linear Regression
- Logistic Regression
- Polynomial Regression
Linear Regression is the most suitable regression technique for predicting a continuous variable, such as the selling price of houses. It establishes a linear relationship between the independent and dependent variables, making it ideal for this scenario.
Which type of learning would be best suited for categorizing news articles into topics without pre-defined categories?
- Reinforcement learning
- Semi-supervised learning
- Supervised learning
- Unsupervised learning
Unsupervised learning is the best choice for categorizing news articles into topics without predefined categories. Unsupervised learning algorithms can cluster similar articles based on patterns and topics discovered from the data without the need for labeled examples. Reinforcement learning is more suitable for scenarios with rewards and actions. Supervised learning requires labeled data, and semi-supervised learning combines labeled and unlabeled data.
In SVM, what does the term "kernel" refer to?
- A feature transformation
- A hardware component
- A software component
- A support vector
The term "kernel" in Support Vector Machines (SVM) refers to a feature transformation. Kernels are used to map data into a higher-dimensional space, making it easier to find a linear hyperplane that separates different classes.
In the bias-variance decomposition of the expected test error, which component represents the error due to the noise in the training data?
- Bias
- Both Bias and Variance
- Neither Bias nor Variance
- Variance
In the bias-variance trade-off, the component that represents the error due to noise in the training data is both bias and variance. Bias refers to the error introduced by overly simplistic assumptions in the model, while variance represents the error due to model sensitivity to fluctuations in the training data. Together, they account for the expected test error.
CNNs are particularly effective for image data due to their ability to preserve the ________ structure of the data.
- Spatial
- Color
- Temporal
- Frequency
CNNs are effective for image data due to their ability to preserve the spatial structure of the data, which is crucial for detecting patterns in pixels' proximity.
While linear regression is concerned with estimating the mean of the dependent variable, logistic regression estimates the probability that the dependent variable belongs to a particular ________.
- Category
- Class
- Cluster
- Group
Logistic regression estimates the probability that the dependent variable belongs to a particular class or category. Unlike linear regression, which predicts continuous values, logistic regression is used for classification problems.
Which type of learning is typically employed when there's neither complete supervision nor complete absence of supervision, but a mix where an agent learns to act in an environment?
- Reinforcement Learning
- Self-supervised Learning
- Semi-supervised Learning
- Unsupervised Learning
Semi-supervised Learning fits this scenario. It combines labeled and unlabeled data to train a model. In situations where you have some labeled data but not enough for full supervision, or when labeling is expensive, semi-supervised learning is a practical choice.
Why is Independent Component Analysis (ICA) primarily used in applications like audio signal processing?
- It's more computationally efficient
- It separates mixed sources effectively
- It requires less data for training
- It's based on supervised learning
ICA is used in audio signal processing because it can effectively separate mixed sources, making it useful for source separation and blind signal separation tasks.