Crucial Algorithms For Your Data Analysts To Know When Operating Within Machine Learning

• By Madeline Miller
• 16-02-2022
• Data Science

Machine Learning (ML) is on the rise as our technological expertise grows and grows. It is almost certain that ML will play a huge part in the advances that are made in new technologies and advancements. Within the ML world are hundreds of algorithms, all with their own functions and uses. It is becoming a mammoth task to trawl through them to identify the most needed. Knowing what you are looking for before you begin will make this part so much easier to work through and be able to filter out what you don’t need. A career within ML could be a simple more to make if you have been well organized at work. Here are some that are essential in ML and others which are more niche in their requirement. Through this article, there are some of the essentials that your data analysts will require.

Tried and tested linear regression

In a nutshell, linear regression is the best fit line placed within data points of a chart. ML uses this algorithm by taking multiple variables before making an accurate prediction.

This method of analysis is widely used and scores highly in terms of accuracy too.

The relationship or link between predictors and response variables can be expressed as a formula to help explain the positioning of the line of best fit. No matter what the aim of your analysis is, linear regression analysis is an effective algorithm that can produce accurate results.

Let the decision tree guide you

Decision trees have been used for decades due to their ease of understanding and their effectiveness. Once created, an ML decision tree has the effect of branches growing from one root. These are used to categorize problems and enable the user to make a more efficient choice of solution. Essentially, a decision tree breaks a set into its many parts in a systematic way where the user follows the branches to the outcome. Interestingly, decision trees are being created as research opportunities based on the outcomes of the tree.

Support Vector Machine (SVM)

A classification algorithm. The first step in SVM is to plot raw data points onto a chart. A line is then used to separate data points and its position is chosen specifically based on data points. More lines are then added to split the data into sections. A key part of the SVM is the kernel. Quite simply this kernel function can bring problematic computations closer together and produce higher quality analysis. Higher dimensions are created with the data and it is this data that is used to create something called a hyperplane that enables analysis to happen in different dimensions. SVMs are able to classify both linear and nonlinear data effectively and accurately.

Clustering by K-means

Data sets are put into clusters and then analyzed in their smaller parts of the whole data set. The cluster analysis then creates clusters within clusters which produces very accurate discussion points within the cluster groups that are created. K-means clustering is often described as simple but effective in terms of ML.

Marketing Department Apriori

This is often found in the market analysis departments. Combinations of frequently occurring data enable positive and negative relationships between data points. Sales departments find this type of analysis extremely useful when identifying products that certain consumers use. This is then used to better focus their sales directions moving forward.

Random Forest

This data analysis algorithm is very appropriately named. Quite simply, it is a series of decision tree guides all being used and linked simultaneously.

A huge benefit of this is that the chances of errors are reduced significantly. More complex to organize but produces much more accurate results.

Within the random forest, some trees are removed from the analysis as they are deemed to be unsuitable to the ML. This action is what is paramount in creating accurate results. Anything that will hinder this is either edited and modified or removed from the algorithm completely.

Algorithms generally are becoming more secure and accurate. This is also the case with machine learning algorithms the world over. They are becoming more efficient, more powerful and fast to work through. It is important to remember that the robot works for you not alongside or above you. They are there to make your life better and more productive but complete tasks in and around the home.