Do You Know How to Select an Initial Model for a Data Science Problem?

These initial models for a data science problem can save a lot of time

Most aspiring data science beginners take a lot of time trying to decide what model to use for a problem. So, we are here to provide you with easy ways to use while selecting an initial model. So let's get started and know how to select an initial model for a data science problem in this article.

When wanting to choose from endless variants then there are many models. There are only minimal alterations that are needed to change a regression model into a classification model and the same happens vice-versa too. To make your work easy already there are many packages of standard python supervised learning so you can choose from them. For example, the decision trees, support vector machines (SVM), naive Bayes, K-nearest neighbors, gradient boosting, random forests, and to name many.

There are many popular models like BERT, xgboost, and GPT-3. But while narrowing down things, let's consider starting with a few for the initial model for a data science problem.

Linear Regression

Linear regression is a simple and basic predictive analysis that is commonly used. It is to examine two things: does a set of predictor variables do a good job in guessing the outcome variable and which variables, in particular, are vital predictors of the outcome variable and in what way do they indicate the magnitude, and a sign of the beta estimates impact the outcome variable? This model can be used for selecting the initial model for a data science problem.

Logistic Regression

Logistic regression is the appropriate regression analysis to conduct when the dependent variable is binary. This type is used to describe data and to explain the relationships between one dependent binary variable and one or more being ordinal, interval, ordinal, or ratio-level independent variables.

These models can be a great start since your problems don't need complicated models. Linear and Logistic regression have been studied for decades and are some of the most well-understood models of machine learning. Another added advantage is that these models are easily interpretable.

Ask yourself questions such as…

Is my problem a classification one or a regression one?

Are you making efforts to predict a continuous output?

We will give you a few examples so that you can understand them better. So if you are trying to predict the price of something like a house, a product, or stock then it is Regression. While thinking of how long is the flight duration, or manufacturing time or user spends on a blog, then it is also a regression.

So in such a case start with linear regression and plot your linear regression and evaluate these models. This can save performance time if this is not working then you can start experimenting with the other models.

Is your problem a classification one?

And are you trying to take note of a binary output or multiple unique and discrete outputs?

When you determine something from your store or win a game then it is a classification. So in this case, start with logistic regression and draw a plot of your data and then evaluate the model. This can be of great help.