Pixelette Technologies

How statistical inference is different from machine learning

Statistics and machine learning are highly co-related fields, and it is expected from the machine learning experts to know the subject of stats. The statistical inference is referred to as a method that assists in quantifying the properties of a certain domain or population known as sample.
Regardless of their similarities, the obvious difference between machine learning and statistical inference is:

Purpose

The purpose of both terms varies significantly. A machine learning model is utilized to make more accurate estimations, while statistical models are mainly used to inference relationship between multiple variables.
The other thing that distinguishes between the two terms is that machine learning provides more than one degree of interpretability.

Predictions & Rearward looking

The machine learning models are trained to observe the past data and then make predictions about the present and future data. On the other hand, the statistical models are upskilled to quantify the connection between two variables, and this relationship only exists in the data that was collected in the past and we can just hope that the connection holds in the future as well.
In short, machine learning can be used to project patterns for the coming time while statistical models are more suitable in defining the pattern in the process of data collection.

Small vs Big set of data

Looking in general at both concepts, the machine learning models are better in handling a small set of data and statistical inference is better for large dataset.
But again, this hypothesis is not accurate. We have come across many machine learning models where we have seen accurate predictions for small data as well.
Regardless of performing well on the big dataset, the accuracy of the statistical inference model does not improve with the addition of more observations.

Amount of variables

Machine learning models are capable of differentiating between the variables, which are relevant and which variables will only create noise in prediction. While in the statistical inference the models are not able to distinguish between variables, hence, accuracy is not there.
In the case where predictors are more than observations, the statistical models tend to fail.

Where to get the best machine learning services?

After getting to know the importance of machine learning in dataset handling, the next decision is to choose a place to get these services.
Pixelette Technologies develops advance machine learning models to predict the unknown variables and to assist in handling the big sets of data.

Conclusion

So, if your purpose is dealing and predicting the big datasets and multiple variables, machine learning models will be a rational choice. But if the purpose is an only explanation rather than prediction, statistical inference is a way to go

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