Every day we use more internet, there are more devices connected to the network, and the increase in storage capacity is higher. However, not so many companies implement AI or artificial intelligence technologies in the study of data.
According to a Forbes study in 2020, only 13% of global companies use the data they possess efficiently.
With data science, artificial intelligence or Machine Learning, companies can take advantage of data and information to make decisions faster and more efficiently.
Machine Learning helps us make predictions based on the probabilities of what will happen after observing and understanding history.
But we can ask ourselves why this information is helpful to a company.
What use is data to a company?
When we talk about what data science is for, we could simplify by saying that “it allows us to answer questions using data.”
In principle, AI serves to reduce costs and increase efficiency. In addition, it allows answering questions through the use of data. For example, how much money did we make last year? “How much money will we make this year?”
The final goal is to create new knowledge based on the predictions generated by artificial intelligence algorithms through the observation and analysis of data.
An algorithm is a procedure used to solve a problem or perform a calculation. Algorithms are an exact list of instructions that perform specific step-by-step actions in hardware- or software-based routines. Algorithms are widely used in all areas of IT. They are like a list of pre-established instructions that guide the decisions.
How do we choose the correct algorithm?
There is no sure answer to this question, but it depends on many factors, such as the type and size of the data, the observation of the data, etc.
In principle, collecting a good amount of data is recommended to obtain reliable predictions.
smallest data set
If the training data is smaller or the dataset has fewer observations and more features, choose algorithms with high bias/low variances, such as linear regression, Naive Bayes, or linear SVM.
We briefly explain these AI algorithms:
- Linear Regression: In this process, a relationship between independent and dependent variables is established by fitting them to a line. This line is known as the regression line and is represented by a linear equation Y=a*x+b
- Naive Bayes Algorithm: A Naive Bayes classifier assumes that a particular class feature is unrelated to other features’ presence.
- Linear SVM In the SVM (Support Vector Machine) algorithm, we plot raw data as points in n-dimensional space (n equals the number of features you have). The value of each element is linked to a particular coordinate, making it easier to classify the data.
Large enough amount of data
Suppose your training data is large enough, and the number of observations is more significant than the number of features. You can opt for low-deviation/high-variance algorithms like KNN, decision trees, or SVM kernel.
KNN. This algorithm can be applied to classification and regression problems. It stores all available cases and ranks any new circumstances by voting from its K neighbors. The point is then assigned to the class with which it has the most in common.
Decision tree: It is a supervised learning algorithm used for classification problems. This algorithm divides the population into two or more homogeneous sets based on the most significant attributes and independent variables.
KERNEL SVM Algorithm: The Kernel function typically transforms the training dataset so that a nonlinear decision surface can be transformed into a linear equation over a more significant number of dimension spaces.
We hope that with this article, you have understood how and why artificial intelligence algorithms are used in companies and when each should be used if you have questions, contact conanexiles-database.