Contents
What are the different types of machine learning models?
Let’s dive into it. Fundamental Segmentation of Machine Learning Models. All machine learning models are categorized as either supervised or unsupervised. If the model is a supervised model, it’s then sub-categorized as either a regression or classification model. We’ll go over what these terms mean and the corresponding models
When to use a regression model in machine learning?
This is another one of the types of regression in machine learning which is usually used when there is a high correlation between the independent variables. This is because, in the case of multi collinear data, the least square estimates give unbiased values. But, in case the collinearity is very high, there can be some bias value.
Why do I get different results each time in machine learning?
Differences in the development environment, such as software versions and CPU type, can cause rounding error differences in predictions and model evaluations. Let’s get started. Why Do I Get Different Results Each Time in Machine Learning?
Do you need a statistical hypothesis test for machine learning?
Although not foolproof, statistical hypothesis testing can improve both your confidence in the interpretation and the presentation of results during model selection. In this tutorial, you will discover the importance and the challenge of selecting a statistical hypothesis test for comparing machine learning models.
Why do we need categorical data in machine learning models?
All machine learning models are some kind of mathematical model that need numbers to work with. This is one of the primary reasons we need to pre-process the categorical data before we can feed it to machine learning models. Let’s consider following data set:
Can a machine learning model be trained without homogeneous data?
We can not build effective supervised machine learning models (models that need to be trained with manually curated or labeled data) without homogeneous data. Clustering helps us achieve this in a smarter way.
Which is an example of a machine learning ensemble?
Ensembles – Combination of multiple machine learning models clubbed together to get better results. In the machine, learning regression is a set of problems where the output variable can take continuous values. For example, predicting the airline price can be considered as a standard regression task.