AIA Quiz

Machine Learning uses algorithms that can learn from data without relying on explicitly programmed methods.

• True

• False

Which are the two types of supervised learning techniques?

• Classification and Clustering

• Classification and K-Means

• Classification and Regression

• Regression and Partitioning

Which of the following statements best describes the Python scikit library?

• A library for scientific and high-performance computation.

• A collection of algorithms and tools for machine learning.

• A library that provides high-performance, easy to use data structures.

• A collection of numerical algorithms and domain-specific toolboxes.

Answer explanation

The Python scikit-learn library (often referred to as scikit-learn or sklearn) is best described as:

A machine learning library for Python that provides simple and efficient tools for data analysis and modeling.

Training and testing on the same dataset might have a high training accuracy, but its out-of-sample accuracy might be low.

• True

• False

If the correlation coefficient is 0.7 or lower, it may be appropriate to fit a non-linear regression.

• True

• False

Answer explanation

The correlation coefficient, often denoted as "r" or "ρ" (rho), is a statistical measure that quantifies the strength and direction of a linear relationship between two continuous variables. It assesses how well the variation in one variable can be explained by the variation in another variable. The correlation coefficient takes values in the range of -1 to 1

When we should use Multiple Linear Regression?

• When we would like to identify the strength of the effect that the independent variables have on a dependent variable.

• When there are multiple dependent variables.

Answer explanation

Multiple Linear Regression is a statistical and machine learning technique used when you want to establish a linear relationship between a dependent variable (target) and multiple independent variables (features or predictors).

In K-Nearest Neighbors, which of the following is true:

• 
  

  A very high value of K (ex. K = 100) produces an overly generalised model, while a very low value of k (ex. k = 1) produces a highly complex model.

• A very high value of K (ex. K = 100) produces a model that is better than a very low value of K (ex. K = 1)

• A very high value of k (ex. k = 100) produces a highly complex model, while a very low value of K (ex. K = 1) produces an overly generalized model.

Answer explanation

In K-Nearest Neighbors (K-NN), the "k" value represents the number of nearest neighbors to consider when making a prediction or classification for a new data point.

Increasing the "k" value tends to create smoother decision boundaries in the classification space. This is because the prediction or classification is based on a larger number of neighboring data points, which can result in a more generalized model.

Choosing the appropriate "k" value in K-NN is often a trade-off between bias and variance. Lower "k" values (e.g., 1 or 3) tend to be more sensitive to noise but can capture local patterns well. Higher "k" values (e.g., 10 or 20) provide a more stable and generalized model but may miss fine-grained details.

A classifier with lower log loss has better accuracy.

• True

• False

When building a decision tree, we want to split the nodes in a way that decreases entropy (impurity) and increases information gain.

• True

• False

Answer explanation

When building a decision tree in machine learning, the goal is to create a tree structure in which nodes are split in a way that decreases entropy (or impurity) and increases information gain. This process is a fundamental part of decision tree algorithms

Which one is NOT TRUE about k-means clustering?

• K-means divides the data into non-overlapping clusters without any cluster internal structure.

• The objective of k-means is to form clusters in such a way that similar samples go into a cluster and dissimilar samples fall into different clusters.

• As k-means is an iterative algorithm, it guarantees that it will always converge to the global optimum.

Answer explanation

K-means clustering is a popular unsupervised machine learning algorithm used for partitioning a dataset into a set of clusters or groups based on the similarity of data points. The primary goal of K-means clustering is to divide the data into clusters in such a way that data points within the same cluster are more similar to each other than to those in other clusters.