In machine learning, while building predictive models we often come to a situation where we have fewer data. What to do in such scenarios? Do we need a very strong predictive model or more data to build our model? It is often said more data will always result in good performance of a model. But is it correct?

Through this article, we will experiment with a classification model by having datasets of different sizes. We will build a model with less no of data samples and then more no of data samples and then check their accuracy scores. For this, we are going to use the Wine Dataset that is available on Kaggle.

What we will learn from this?

• How the size of the data impacts the accuracy of a classification model?
• Comparison of model accuracy with less and more number of data samples

#developers corner #classification #classification accuracy #logistic regression #machine learning #parameter tuning