Difference between parameters, features and class in Machine Learning

Question

I am a newbie in Machine learning and Natural language processing.

I am always confused between what are those three terms?

From my understanding:

class: The various categories of our model output. Given the name of the person identify whether he/she is male or female?

Let's say I am using a Naive Bayes classifier.

What would be my features and parameters?

Also, what are some of the aliases of the above words which are used interchangeably?

Thank you

Answer 1

Let's use the example of classifying the gender of a person. You give an input observation, our Naive Bayes Classifier should output a category. 

Features: 

In Naive Bayes Classifier, or any general ML Classification Algorithm, the data points we choose to define our input. 

For example:

We can't possibly input all data points about a person; instead, we pick a few features to define a person. In a Naive Bayes Classifier, the key assumption we make is that these features are independent of a person's height doesn't affect weight doesn't affect foot size. This assumption may or not be true, but for a Naive Bayes, we assume that it is true.

Parameters: Parameters in Naive Bayes are the estimates of the true distribution of classified points. 

For example: 

We can say that roughly 50% of people are male, and the distribution of male height is a Gaussian distribution with mean 5' 7" and standard deviation 3". The parameters would be the 50% estimate, the 5' 7" mean estimate, and the 3" standard deviation estimate.

More details could be found out while studying Machine Learning Tutorial and Deep Learning Tutorial.

I hope that was helpful!

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