Sage-Tips

What is iid assumption in machine learning?

In machine learning theory, i.i.d. assumption is often made for training datasets to imply that all samples stem from the same generative process and that the generative process is assumed to have no memory of past generated samples.

Why is iid assumption needed?

So in a way the assumption of I.I.D helps simplify training machine learning algorithms by assuming that the data distribution won’t change over time or space and sample wont be dependent on each other in anyway.

Does random forest assume iid?

There are also specific techniques that assume I.I.D., such as bootstrap aggregating (bagging) models or cross-validation. Bagging specifically, like random forests, uses random subsamples of the data to generate many different models that are then combined or averaged together to reduce variance and overfitting.

What are IID samples?

IID Statistics and Random Sampling A random sample can be thought of as a set of objects that are chosen randomly. Or, more formally, it’s “a sequence of independent, identically distributed (IID) random variables”. In statistics, we usually say “random sample,” but in probability it’s more common to say “IID.”

How do you check if samples are IID?

Note that simple random sampling is sampling without replacement and thus the observations comprising the sample are not independent. However, if the sample size n is small compared to the population size, then the observations are approximately independent and so a simple random sample is approximately IID.

Why is it important for sample data to be IID in hypothesis testing?

Identically distributed data are vital for most hypothesis tests because they indicate you are assessing a stable phenomenon.

Why Modelling assumptions are important in building a model?

Checking model assumptions is essential prior to building a model that will be used for prediction. If assumptions are not met, the model may inaccurately reflect the data and will likely result in inaccurate predictions.

What are the assumptions of Random Forest model?

ASSUMPTIONS. No formal distributional assumptions, random forests are non-parametric and can thus handle skewed and multi-modal data as well as categorical data that are ordinal or non-ordinal.

What assumptions are made when drawing a decision tree?

Assumptions while creating Decision Tree In the beginning, the whole training set is considered as the root. Feature values are preferred to be categorical. If the values are continuous then they are discretized prior to building the model. Records are distributed recursively on the basis of attribute values.

How do you know if a sample is IID?

The sample is IID if the random variables have the following two properties: Independent: The random variables X1,X2,…,Xn are independent. P(a ≤ X ≤ b ∩ c ≤ Y ≤ d) = P(a ≤ X ≤ b)P(c ≤ Y ≤ d).

How does sample size affect machine learning results?

Generally, the higher the ratio of features to sample size the more likely that an ML model will fit the noise in the data instead of underlying pattern [ 1, 6, 8 ]. Similarly, the higher the number of adjustable parameters the more likely the ML model is to overfit the data [ 9 ].

Does high-dimensional data lead to biased machine learning?

High dimensional data with a small number of samples is of critical importance for identifying biomarkers and conducting feasibility and pilot work, however it can lead to biased machine learning (ML) performance estimates.

Why is it important to understand the logic behind machine learning?

Similarly in machine learning, appreciating the assumed logic behind machine learning techniques will guide you toward applying the best tool for the data. By Vishal Mendekar, Skilled in Python, Machine Learning and Deep learning.

Can machine learning predict autism from non-autistic individuals?

Our review of studies which have applied ML to predict autistic from non-autistic individuals showed that small sample size is associated with higher reported classification accuracy. Thus, we have investigated whether this bias could be caused by the use of validation methods which do not sufficiently control overfitting.