Snowflake SnowPro Advanced: Data Scientist Certification DSA-C02 Exam Questions

Supervised Learning

Overview:

Supervised learning is a type of machine learning that uses labeled data to train machine learning

models. In labeled data, the output is already known. The model just needs to map the inputs to the

respective outputs.

Algorithms:

Some of the most popularly used supervised learning algorithms are:

· Linear Regression

· Logistic Regression

· Support Vector Machine

· K Nearest Neighbor

· Decision Tree

· Random Forest

· Naive Bayes

Working:

Supervised learning algorithms take labelled inputs and map them to the known outputs, which

means you already know the target variable.

Supervised Learning methods need external supervision to train machine learning models. Hence,

the name supervised. They need guidance and additional information to return the desired result.

Applications:

Supervised learning algorithms are generally used for solving classification and regression problems.

Few of the top supervised learning applications are weather prediction, sales forecasting, stock price

analysis.

Supervised learning methodology applies conditional probability of all the variables with respective

the dependent variable and generally conditional probability of variables is nothing but a basic

method of estimating the statistics for few random experiments.

Conditional probability is thus the likelihood of an event or outcome occurring based on the

occurrence ofsome other event or prior outcome. Two events are said to be independent if one

event occurring does not affect the probability that the other event will occur.

What is linear regression?

Linear regression analysis is used to predict the value of a variable based on the value of another

variable. The variable you want to predict is called the dependent variable. The variable you are

using to predict the other variable's value is called the independent variable.

Linear regression attempts to model the relationship between two variables by fitting a linear

equation to observed data. One variable is considered to be an explanatory variable, and the other is

considered to be a dependent variable. For example, a modeler might want to relate the weights of

individuals to their heights using a linear regression model.

A linear regression line has an equation of the form Y = a + bX, where X is the explanatory variable

and Y is the dependent variable. The slope of the line is b, and a is the intercept (the value of y when

x = 0).

Forlinearregression Y=a+bx+error.

If neglect error then Y=a+bx. If x increases by 1, then Y = a+b(x+1) which implies Y=a+bx+b. So Y

increases by its slope.

For linear regression Y=a+bx+error. If neglect error then Y=a+bx. If x increases by 1, then Y = a+b(x+1)

which implies Y=a+bx+b. So Y increases by its slope.

The Supervised Machine Learning algorithm can be broadly classified into Regression and

Classification Algorithms. In Regression algorithms, we have predicted the output for continuous

values, but to predict the categorical values, we need Classification algorithms.

What is the Classification Algorithm?

The Classification algorithm is a Supervised Learning technique that is used to identify the category

of new observations on the basis of training data. In Classification, a program learns from the given

dataset or observations and then classifies new observation into a number of classes or groups. Such

as, Yes or No, 0 or 1, Spam or Not Spam, cat or dog, etc. Classes can be called as targets/labels or

categories.

Unlike regression, the output variable of Classification is a category, not a value, such as "Green or

Blue", "fruit or animal", etc. Since the Classification algorithm is a Supervised learning technique,

hence it takes labeled input data, which means it contains input with the corresponding output.

In classification algorithm, a discrete output function(y) is mapped to input variable(x).

y=f(x), where y = categorical output

The best example of an ML classification algorithm is Email Spam Detector.

The main goal of the Classification algorithm is to identify the category of a given dataset, and these

algorithms are mainly used to predict the output for the categorical data.

The algorithm which implements the classification on a dataset is known as a classifier. There are two

types of Classifications:

Binary Classifier: If the classification problem has only two possible outcomes, then it is called as

Binary Classifier.

Examples: YES or NO, MALE or FEMALE, SPAM or NOT SPAM, CAT or DOG, etc.

Multi-class Classifier: If a classification problem has more than two outcomes, then it is called as

Multi-class Classifier.

Example: Classifications of types of crops, Classification of types of music.

Binary classification in deep learning refers to the type of classification where we have two class

labels – one normal and one abnormal. Some examples of binary classification use:

· To detect whether email is spam or not

· To determine whether or not a patient has a certain disease in medicine

· To determine whether or not quality specifications were met when it comes to QA (Quality

Assurance).

For example, the normal class label would be that a patient has the disease, and the abnormal class

label would be that they do not, or vice-versa.

As is with every other type of classification, it is only as good as the binary classification dataset that

it has – or, in other words, the more training and data it has, the better it is.

Binary vs. Multi-Class Classification

Classification problems are common in machine learning. In most cases, developers prefer using a

supervised machine-learning approach to predict class tables for a given dataset. Unlike regression,

classification involves designing the classifier model and training it to input and categorize the test

dataset. For that, you can divide the dataset into either binary or multi-class modules.

As the name suggests, binary classification involves solving a problem with only two class labels. This

makes it easy to filter the data, apply classification algorithms, and train the model to predict

outcomes. On the other hand, multi-class classification is applicable when there are more than two

class labels in the input train data. The technique enables developers to categorize the test data into

multiple binary class labels.

That said, while binary classification requires only one classifier model, the one used in the multiclass approach depends on the classification technique. Below are the two models of the multi-class

classification algorithm.

One-Vs-Rest Classification Model for Multi-Class Classification

Also known as one-vs-all, the one-vs-rest model is a defined heuristic method that leverages a binary

classification algorithm for multi-class classifications. The technique involves splitting a multi-class

dataset into multiple sets of binary problems. Following this, a binary classifier is trained to handle

each binary classification model with the most confident one making predictions.

For instance, with a multi-class classification problem with red, green, and blue datasets, binary

classification can be categorized as follows:

Problem one: red vs. green/blue

Problem two: blue vs. green/red

Problem three: green vs. blue/red

The only challenge of using this model is that you should create a model for every class. The three

classes require three models from the above datasets, which can be challenging for large sets of data

with million rows, slow models, such as neural networks and datasets with a significant number of

classes. The one-vs-rest approach requiresindividual modelsto prognosticate the probability-like score. The class index with the largest score is then used to predict a class. As such, it is commonly used for

classification algorithms that can naturally predict scores or numerical class membership such as

perceptron and logistic regression.