Class Three Flashcards
What is AI?
Artificial Intelligence (AI) is a branch or Computer Science that uses algorithms and techniques to mimic human intelligence
What is ML?
Machine Learning (ML) is one of several AI techniques for sophisticated cognitive tasks
What is supervised machine learning?
Supervised machine learning is a type of machine learning where the algorithm learns from labeled training data to make predictions or classify new, unseen data.
What are the three approaches to AI?
Symbolic, Statistical, and ML (chess game)
What is K-Nearest Neighbors (KNN)?
K-Nearest Neighbors (KNN) is a supervised machine learning algorithm that classifies data points based on their proximity to the K nearest neighbors in the training set.
What are the advantages of K-Nearest Neighbors (KNN)?
Advantages of KNN include its simplicity, ability to handle multi-class classification, and flexibility in handling both numerical and categorical data.
When should you use K-Nearest Neighbors (KNN)?
KNN is suitable when the data has well-defined clusters, and when the decision boundary is expected to be nonlinear or complex.
How does K-Nearest Neighbors (KNN) algorithm determine the class of a data point?
KNN determines the class of a data point by majority voting among its K nearest neighbors, where each neighbor contributes one vote.
What are the limitations of K-Nearest Neighbors (KNN)?
Limitations of KNN include its computational complexity for large datasets, sensitivity to the choice of K and distance metric, and the need for proper scaling of features.
What is linear regression?
Linear regression is a supervised machine learning algorithm used to model the relationship between a dependent variable and one or more independent variables, assuming a linear relationship.
What are the advantages of linear regression?
Advantages of linear regression include its interpretability, simplicity, and the ability to quantify the strength and direction of relationships between variables. Furthermore, you can take large amounts of raw data and transform it into actionable insights, and we can uncover patterns and relationships.
When should you use linear regression?
Linear regression is suitable when there is a linear relationship between the dependent and independent variables, and when the goal is to predict a continuous target variable.
What are the assumptions of linear regression?
Linear regression assumes a linear relationship between the dependent and independent variables, independence of errors, homoscedasticity (constant variance of errors), and absence of multicollinearity.
What are the limitations of linear regression?
Limitations of linear regression include its assumption of linearity, sensitivity to outliers, reliance on independence and homoscedasticity assumptions, and inability to capture complex relationships.
Disadvantages:
* Performs poorly when there are non-linear relationships.
* Sensitive to outliers.
What is logistic regression?
Logistic regression is a supervised machine learning algorithm that models the relationship between the independent and dependent variable and estimates the probability of an event occurring.
