Supervised vs Unsupervised Learning

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Questions and Answers

What does supervised machine learning heavily rely on?

Random data generation

Historical data (correct)

Real-time data feeds

Unstructured data

Which of the following describes the relationship between independent variable x and dependent variable y in supervised learning?

y is irrelevant to x

y increases disproportionately with changes in x

y increases with an increase in x (correct)

y decreases as x decreases

What type of machine learning does NOT require historical data?

Predictive learning

Supervised learning

Reinforcement learning

Unsupervised learning (correct)

Which of the following algorithms is mentioned as part of the unsupervised learning techniques?

K-means Signup and view all the answers

How is traditional computer programming fundamentally different from machine learning in predicting outputs?

Machine learning adjusts its approach based on input data. Signup and view all the answers

What is the primary function of the Spark machine learning libraries?

Implementation of machine learning algorithms Signup and view all the answers

In machine learning terms, what is the role of the y variable?

Target variable for predictions Signup and view all the answers

What characteristic of Spark makes it suitable for big data processing?

Distributed and resilient computing Signup and view all the answers

What is the primary goal of supervised machine learning?

To develop a predictor function based on input and output data Signup and view all the answers

What does the predictor function represent in the context of supervised learning?

The true relationship between input and output variables Signup and view all the answers

How is prediction error defined in supervised learning?

The difference between predicted and actual values for any input variable Signup and view all the answers

In supervised learning, what represents the target variable that the hypothesis aims to model?

The dependent variable, y Signup and view all the answers

Which method is used to separate data points into discrete sets in supervised learning?

Classification Signup and view all the answers

What happens to prediction error when the hypothesis closely fits the training data?

It decreases Signup and view all the answers

What is the term used for the function that is used to predict the outcome of the dependent variable in supervised learning?

Hypothesis Signup and view all the answers

In a linear regression scenario, how is the predicted value of y calculated for a given x?

By drawing a straight line through training data points Signup and view all the answers

What is the primary purpose of regression analysis?

To understand the relationship between dependent and independent variables Signup and view all the answers

In linear regression, what does the slope (b) of the regression line represent?

The rate of change of the dependent variable with respect to the independent variable Signup and view all the answers

What is the least squares method used for in regression analysis?

To minimize the difference between predicted and actual values Signup and view all the answers

What does a positive relationship between the independent variable (x) and dependent variable (y) indicate?

As x increases, y also increases Signup and view all the answers

Which type of regression involves multiple independent variables?

Multiple linear regression Signup and view all the answers

What does the y-intercept (a) in the linear regression equation represent?

The value of y when x is zero Signup and view all the answers

What is a factor that might cause prediction error in regression analysis?

Unaccounted external influences Signup and view all the answers

Which method is commonly employed to visually represent the relationship in regression analysis?

Scatter plot Signup and view all the answers

What does a higher R-squared value indicate about a model?

The model fits the training data well. Signup and view all the answers

What range does R-squared values fall within?

0% to 100% Signup and view all the answers

What does a high standard error indicate about sample means?

They are widely spread around the population mean. Signup and view all the answers

In a generalized linear model, what does 'k' represent in the equation y = a0 + b1x1 + b2x2 + ... + bkxk?

The number of independent variables. Signup and view all the answers

Which statement is true regarding logistic regression?

It is used for binary classification of output variables. Signup and view all the answers

What is the purpose of using standard error in regression analysis?

To measure how well sample data represents the whole population. Signup and view all the answers

Which of the following describes the logistic regression analysis purpose?

To predict binary outcomes based on input variables. Signup and view all the answers

What is the formula for calculating R-squared?

R-squared = explained variance / total variance Signup and view all the answers

What is a characteristic of classification in supervised learning?

Classes are known and predefined. Signup and view all the answers

What is the purpose of the training set in supervised learning?

To train the model and reduce prediction error. Signup and view all the answers

Which of the following describes Spark's role in data processing?

It provides abstract APIs for big data processing. Signup and view all the answers

What does the driver program in Spark do?

Is responsible for launching and maintaining Spark applications. Signup and view all the answers

In supervised learning, what typically represents the process of validation?

Checking for accuracy using a validation set. Signup and view all the answers

What do Spark executor processes primarily do?

Run tasks assigned by the driver and manage in-memory data. Signup and view all the answers

What is the typical split rule used for training and validation sets?

80-20 Signup and view all the answers

What is one role of the cluster manager in Spark?

It is responsible for physical machines and resource allocation. Signup and view all the answers

Study Notes

Supervised and Unsupervised Machine Learning

Machine learning is categorized into two types: supervised and unsupervised learning.
Supervised learning algorithms rely on historical data to make predictions on new data points.
Unsupervised learning is used when there is no historical data available.

Supervised Learning (Linear Regression)

Linear regression models the relationship between a dependent variable (y) and one or more independent variables (x).
The relationship between x and y is represented by a straight line.
The predictor function is a hypothesis that attempts to model the relationship between x and y.
The goal of supervised learning is to minimize prediction error, the difference between the predicted value and the actual value.

Classification

Classification separates data points into discrete sets known as classes, types, or categories.
It defines a decision boundary that separates the output variables into different classes.
Email classification is an example of a classification task.
The generic supervised learning process involves splitting data into training and validation sets to train and evaluate the model.

The Spark Programming Model

Spark is a distributed in-memory processing engine that handles large data volumes using Resilient Distributed Datasets (RDDs).
Spark consists of three components:
- Driver: launches and manages Spark applications, schedules tasks, and distributes data.
- Executor: runs tasks, stores data in RDDs, and reports execution status.
- Cluster Manager: allocates resources to Spark applications.

Regression Analysis

Regression analysis predicts or forecasts the occurrence of an event or the value of a continuous variable based on independent variables.
It identifies which variables influence the outcome and their impact.

Linear Regression

Simple linear regression models the relationship between a dependent variable (y) and one independent variable (x).
Multiple linear regression involves multiple independent variables.
The regression line represents the relationship between x and y and can be plotted on an x-y dimension.

Least Square Method

The least square method minimizes prediction error by finding the regression line that best fits the training data.

R-squared

R-squared measures the goodness of fit of a model.
It represents the proportion of variability in the dependent variable that is explained by the independent variables.
R-squared values range from 0% to 100%, with higher values indicating a better fit.

Standard Error

Standard error measures the accuracy of the model by assessing how closely the sample data represents the population.
A high standard error suggests a wide spread of data points and a less representative sample.
A low standard error indicates a close distribution of data points and a more accurate representation.

Generalized Linear Model

Generalized linear models handle situations with multiple dependent variables and correlation among predictor variables.
This model can be used to predict a single dependent variable.

Logistic Regression - Classification Technique

Logistic regression analyzes input variables to predict the binary classification of output variables.
It is used for scenarios where the output variable has two possible outcomes, such as predicting whether an email is spam or not.

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Description

This quiz explores the fundamental concepts of supervised and unsupervised machine learning, focusing on linear regression and classification. Understand how these techniques differ and their applications in data analysis. Test your knowledge on key terms and principles in machine learning.