Welcome to buy time in three hours.
Hello, everyone, this is lecturer seven data repositories and data split in machine learning.
By the end of this lecture, you will be able to comprehend some machine learning data repository jargons,
describe the training and testing steps in machine learning, and describe rapid and random sampling
in machine learning.
Machine learning mimics the way the human brain learns from the data it receives through the sensory
inputs like seeing, hearing, sensing, machine learning requires data to learn, which is referred
to as the training data and data to evaluate how well it learned.
And that is called the testing or evaluation data.
Think about it as a baby that learns what she or he sees or hears, distinguishes differences, interprets
the data, identifies pattern and information from the data, extracts features from the data and response
accordingly.
In general, there are two types of machine learning models.
One of them that we want to focus on is the supervised machine learning techniques in supervised machine
learning problems.
Data with the training or testing has two parts, inputs and outputs.
For example, a student needs to estimate the comparative strengths of materials mathematically, and
his or her results can be compared with actual results from a lab experiment.
There are two types of supervised machine learning problems.
One type is classifiers for classification problems where the outputs are in the form of categories,
classes or integers.
An example of a classification problem can be the classification of cracks in materials.
The other one is rigorousness for regression problems, where the outputs are the form of real values.
An example of a regression problem can be the estimation of housing prices in the future, where the
prices are basically continuous real values.
Our goal in this lecture is to teach you some jargons in machine learning and data processing and the
way we refer to different data in machine learning.
And in the future we will talk about neural networks.
So this lecture is more about data processing and also machine learning in general.
Let's start by defining some popular machine learning jargon we are going to use in discourse whether
training or testing a data point includes inputs and also outputs.
Each of these big metrics represents a data point, and it has two segments.
The inputs and outputs within each columns of inputs represents a predictor or an attribute that addresses
a characteristic, for example, the weight of an animal or the total of number of bedrooms in housing
prices.
Problem, a data repository, includes a number of data points, for example, to any data points that
represents cats and dogs.
Repository data includes both training and testing.
Data points are less distinguished.
For example, training repository that is being used for the purpose of training only or testing repository
that is being used for the purpose of evaluation or testing only similar goals for training and testing
data points, inputs or outputs.
Let's say we have a data repository with 20 data points, each with only two dimensional input and one
dimensional output.
So we have X1 and X2 as our attributes to columns of data.
Hence, we can present the input data in a two dimensional coordinate system where the horizontal axis
shows the first component of inputs X1 and the vertical axis present, the second component of inputs
x2 to train and test the machine learning model.
We need to divide the data points into training and testing data points like this figure.
We train the machine learning with the training data and then evaluate it with the testing data.
When we train the machine learning or we train our student or baby, we don't show them the testing
data, we set them aside.
The testing data is like the data that at the exam date our students can receive.
So they are not being seen until the end of the course or until when our machine learning or our student
learn these data points very well.
In this particular example here, the rate of testing is twenty percent, meaning that twenty percent
of data points in the repository is randomly selected and set aside for evaluation here, and that is
for data points to be exact, and training the model with the remaining 80 percent data points that
are like sixteen data points here if this is a classification.
No problem, I mean, if the outputs are categorical, then we measure the accuracy of the estimation,
that is the number of correctly estimated evaluation data points divided by the total number of evaluation
data points.
Note that here the term evaluation data point is the same as testing data points.
If the problem is a regression problem, then we can measure the accuracy of the model using a mean
squared error MSE or mean absolute error and a.
Let's take a break and then continue to the next part of this lecture.