Google News
logo
Machine Learning Interview Questions
It is the process of reducing random variables under consideration. Dimensionality reduction can be classified as feature selection and feature extraction.
 
Feature selection tries to find the subset of input variables, while feature extraction begins from an initial set of measured data and builds derived values.
Boltzmann Machines have a simple learning algorithm that helps to discover exciting features in training data. These were among the first neural networks to learn internal representations and are capable of solving severe combinatory problems.
Different types of Genetic Programming are :
 
* Grammatical Evolution
* Tree-based Genetic Programming
* Stack-based Genetic Programming
* Linear Genetic Programming (LGP)
* Cartesian Genetic Programming (CGP)
* Extended Compact Genetic Programming (ECGP)
* Genetic Improvement of Software for Multiple Objectives (GISMO)
* Probabilistic Incremental Program Evolution (PIPE)
* Strongly Typed Genetic Programming (STGP)
A regression model that uses L1 Regularization is called Lasso Regression, and the Model which uses L2 Regularization is called Ridge Regression.

* L1 regularization helps in eliminating the features that are not important.
* L1 regularization adds the penalty term in the cost function by adding the absolute value of weight (Wj), while L2 regularization adds the squared value of weights (Wj) in the cost function.
* One more difference between both of them is that L1 regularization tries to estimate the median of the data while L2 regularization tries to estimate the mean of the data.
It is a technique for increasing the model performance by feeding multiple sample data from the dataset. The sampling process is done by breaking the data into smaller parts that have the same number of rows. Out of all the parts, one is randomly selected for the test and another one for train sets.

It consists of the following techniques :
 
* Holdout method
* k-fold cross-validation
* Stratified k-fold cross-validation
* Leave p-out cross-validation
* Gini Impurity and Entropy are the metrics used for deciding how to split a Decision Tree.
* Gini measurement is the probability of a random sample being classified correctly if you randomly pick a label according to the distribution in the branch.
* Entropy is a measurement to calculate the lack of information. You calculate the Information Gain (difference in entropies) by making a split. This measure helps to reduce the uncertainty about the output label.
* Entropy is an indicator of how messy your data is. It decreases as you reach closer to the leaf node.
* The Information Gain is based on the decrease in entropy after a dataset is split on an attribute. It keeps on increasing as you reach closer to the leaf node.
From the perspective of inductive learning, we are given input samples (x) and output samples (f(x)) and the problem is to estimate the function (f). Specifically, the problem is to generalize from the samples and the mapping to be useful to estimate the output for new samples in the future.
 
In practice it is almost always too hard to estimate the function, so we are looking for very good approximations of the function.
 
Some practical examples of induction are :
 
Credit risk assessment.
* The x is the properties of the customer.
* The f(x) is credit approved or not.

Disease diagnosis.
* The x are the properties of the patient.
* The f(x) is the disease they suffer from.

Face recognition.
* The x are bitmaps of peoples faces.
* The f(x) is to assign a name to the face.

Automatic steering.
* The x are bitmap images from a camera in front of the car.
* The f(x) is the degree the steering wheel should be turned.
There are problems where inductive learning is not a good idea. It is important when to use and when not to use supervised machine learning.
 
4 problems where inductive learning might be a good idea :
 
Problems where there is no human expert. If people do not know the answer they cannot write a program to solve it. These are areas of true discovery.

Humans can perform the task but no one can describe how to do it. There are problems where humans can do things that computer cannot do or do well. Examples include riding a bike or driving a car.

Problems where the desired function changes frequently. Humans could describe it and they could write a program to do it, but the problem changes too often. It is not cost effective. Examples include the stock market.

Problems where each user needs a custom function. It is not cost effective to write a custom program for each user. Example is recommendations of movies or books on Netflix or Amazon.
Deductive learning is a subclass of machine learning that studies algorithms for learning provably correct knowledge. Typically such methods are used to speedup problem solvers by adding knowledge to them that is deductively entailed by existing knowledge, but that may result in faster solutions.