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Business Central – Filter Information
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Business Central – Sort Information
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Business Central – Search and Select Information
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Business Central – My Settings
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Business Central – Search
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Business Central – User Interface
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Basics of Machine Learning
In this article we will go through the basics of ML terminologies which will be useful for Data Processing and Data Visualization.
Mean
Median
Mode
Range
Probability
Mean
It is the average of all the values.
| Mean = Sum of all observations / Number of observations |
For example:
Mean = 62800 / 11
= $ 5709.09
Median
It is the numerical middle value of the sorted observations with equal number if observations on both sides.
For example, if we plot the same data as above in ascending order in straight line, you will see that there are equal number of observations on both the sides of “5800”. Hence this is the median value of this sample set.
In case you are wondering about its significance, well, then this helps us understand on which side majority of the observations are tilted. So, in case of data clean-up process if some observations have missing values, we can either replace them with mean or median values.
Mode
It is the value that appears most often in a set of data. In our above sample data, the value “6400” occurs 3 times, which is higher that the occurrences of all the other values. So the mode of this set of observations is “6400”.
This can also be used for replacing missing values in a dataset.
Range
It is the difference of highest and lowest values in a sample of observations. So as per the our sample dataset:
Range = 7000 – 4000
= 3000
This helps us understand how widely the values are spread in a given set of observations.
Probability
This is one of the most important term in machine learning and we all have heard this term in one way or another.
Probability is a numerical way of describing how likely something is going to happen.
Probability is derived from a Sample Space (S). Sample Space is set of possible outcomes that might be observed for an event.
If all this sound Greek to you then let us take a simple example of dice. So when we throw a dice, what are the possible outcome? The only possible outcome is one of the following: 1,2,3,4,5 or 6. So the sample space for dice is the following:
| Dice Sample Space (S) = {1,2,3,4,5,6} |
Now if we want to know what is the Probability of 3, i.e. if we roll the dice, what is the likelihood of getting a 3?
It is 1 out of 6.
| P(A) = 1/6 = 0.1667 OR 16.67% |
Similarly, what is the probability of getting an even number?
| P(A) = 3/6 = 0.5 OR 50% |
Does it make sense now? This is one of the most important topic and many machine learning algorithms such as Naive Bayes, Logistic Regression, etc. are based on fundamental principles of probability.
Stay tuned for more!
Data Types in Machine Learning
In our previous article we looked into the definition of machine learning, how machine learns, how machine learning is different than traditional rule based systems and various machine learning categories, i.e. Supervised, Unsupervised and Reinforcement Learning.
Type of Variables
Data Type
Category
We also know that historical data is crucial for any machine learning algorithms we use, but how to read the data and how to interpret it, so that we can make effective use of it in our algorithm.
Remember garbage in is garbage out.
So, let us understand the data with an example. Let’s say following is the data from a bank which that wants to create an algorithm to determine whether a customer loan should be approved or not.
Now, let’s look at what kind of data we are dealing with here.
In any data set there are three main important factors that we need to understand, before we start working on solving the problem using Machine Learning.:
Type of Variables
So, in this example, we are trying to determine whether the loan application of the customer will be approved or not, in other words the approval is based on the events in the past, because we want our Machine Learning algorithm to predict similar results.
Data Type
At a high level we can safely assume that the data that has been provided to us has some variables as Character/String and some variables as Numeric values. There can be more type or subtype within them, such as integer, float, etc., but as long as we understand these two broad types we are good.
Now, what would happen to the data type, if the bank says that we are going to treat all the customers same, if they have three or more dependents on them. In that case the values above three or the number of dependents does not have any impact and we should change it to “3+”. In such a scenario the “Dependent” variable will become a string type variable.
So, you should pay very close attention to such details coming from the data provider to avoid any data errors in the data processing stage.
Category
Category of the data is a very important aspect. Now, if you look closely some variables contain options such as male, female; yes, no; etc. Such variables are called Categorical variables. Whereas other variables are Continuous variables, where the values can have any possible range.
These are small things but very important for reading and processing the data.
Stay tuned for more articles!
Machine Learning Categories
Machine Learning can be categorized into following categories:
Supervised Learning
Unsupervised Learning
Reinforcement Learning
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
For example, when you were a kid, solutions were provided to you by your parents. These solutions and decisions were based on their past experience. You learnt as you grew. That is Supervised Learning.
However, when you start taking your own decisions without any such result or outcome-based intelligence, that is Unsupervised Learning. Same is true with the machine.
Let’s have a look at it technically.
Supervised Learning
In case of supervised learning:
- The data is labelled, which means both the dependent and independent variables are provided.
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There is an Input variable “X” or set of input variables and an output variable “Y”.
Y = f(X)
- The function is approximated to predict new values of Y given X.
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Examples:
- Regression: Output variable is a real value such as Amount, Height, Weight, etc.
- Classification: Output variable is category, such as Yes, No, red, Blue, Yellow, etc.
| Loan_ID | Gender | Married | Dependents | Self_Employed | Income | LoanAmt | Term | Credit History | Property_Area | Status |
| LP001002 | Male | No | 0 | No | $5849.00 | 60 | 1 | Urban | Y | |
| LP001003 | Male | Yes | 1 | No | $4583.00 | 120 | 1 | Rural | N | |
| LP001005 | Male | Yes | 0 | Yes | $3000.00 | $66.00 | 60 | 1 | Urban | Y |
| LP001006 | Male | Yes | 2 | No | $2583.00 | $120.00 | 60 | 1 | Urban | Y |
Unsupervised Learning
In case of Unsupervised Learning:
- Only X (input) variable is known.
- The goal for Unsupervised Learning is to model the underlying structure or distribution in the data in order to learn more about the data.
- There is no correct answer here and there Is no teacher.
- Algorithms are left on their own to discover and present the interesting structure in the data.
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Example:
- Clustering: Customer behaviour grouping, such as spending patterns, their preferences, their likes and dislikes.
- Association: Recommendation model, for a product which may be liked by a set of customers.
Reinforcement Learning
How do we know if those decisions taken in Unsupervised manner are going to give us desired result? There is where the Reinforcement Learning help us by rewarding the good decisions and penalizing the bad ones.
The idea is to maximize the gain or reward. It’s something like you pay your bills on time and the result is a good credit history.
Similarly, Supervised Learning may group a set of customers correctly using their spending pattern and let’s say now your marketing department wants to show them a set of advertisements. Now, without any past data, how do we maximize the reward or gain, which in this case Is click through rate and this is achieved by reinforcement learning.
Posted in AI & ML
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