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Date: 21-12-2023
Company name: Biocon
Role: ML Engineer
Topic: nodes, adaboost, ensemble, hierarchical

1. List down the different types of nodes in Decision Trees.

The Decision Tree consists of the following different types of nodes:
1. Root node: It is the top-most node of the Tree from where the Tree starts.
2. Decision nodes: One or more Decision nodes that result in the splitting of data into multiple data segments and our main goal is to have the children nodes with maximum homogeneity or purity.
3. Leaf nodes: These nodes represent the data section having the highest homogeneity.

2. What is the Alpha Term in AdaBoost? How does it work?

In AdaBoost, multiple weak learners are trained to get the strong learner. As a result, hence calculating the error term of every weak learner is essential to know which weak learner is performing best and which is not.
The term Alpha is a parameter that indicates the weight that should be given to a particular weak learner algorithm. If the value of the term Alpha for a particular algorithm is high, that indicates that the model is performing best and the error rate for the same is low.

3. Since Ensemble Learning provides better output most of the time, why do you not use it all the time? 

Although it provides a better outcome many times, it is not true that it will always perform better. There are several ensemble methods, each with its own advantages/disadvantages, and choosing one to use depends on the problem at hand. If there are models with high variance, then it will benefit from bagging. If the model is biased, it is better to use boosting. If the work is in probabilistic setting, the ensemble methods may not work because it is known that boosting delivers poor probability estimates.


4. What are the various types of Hierarchical Clustering?

The two different types of Hierarchical Clustering technique are as follows:
Agglomerative: It is a bottom-up approach, in which the algorithm starts with taking all data points as single clusters and merging them until one cluster is left.
Divisive: It is just the opposite of the agglomerative algorithm as it is a top-down approach.

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1. How Are Weights Initialized in a Neural network?

Ans: There are two methods here: we can either initialize the weights to zero or assign them randomly.
Initializing all weights to 0: This makes your model similar to a linear model. All the neurons and every layer perform the same operation, giving the same output and making the deep net useless.

Initializing all weights randomly: Here, the weights are assigned randomly by initializing them very close to 0. It gives better accuracy to the model since every neuron performs different computations. This is the most commonly used method.


2. What are the variants of Gradient descent?

Ans: Stochastic Gradient Descent: We use only a single training example for calculation of gradient and
update parameters.
Batch Gradient Descent: We calculate the gradient for the whole dataset and perform the update at
each iteration.
Mini-batch Gradient Descent: It’s one of the most popular optimization algorithms. It’s a variant of
Stochastic Gradient Descent and here instead of single training example, mini-batch of samples is
used.


3. What are the feature selection methods used to select the right variables?

Ans: There are two main methods for feature selection:
Filter Methods
This involves:
• Linear discrimination analysis
• ANOVA
• Chi-Square
The best analogy for selecting features is "bad data in, bad answer out." When we're limiting or selecting the features, it's all about selecting the useful feature.

Wrapper Methods
This involves:
• Forward Selection: We test one feature at a time and keep adding them until we get a good fit
• Backward Selection: We test all the features and start removing them to see what works better
• Recursive Feature Elimination: Recursively looks through all the different features and how they pair together. Wrapper methods are very labor-intensive, and high-end computers are needed if a lot of data analysis is performed with the wrapper method.

4.  What is joint sampling and separate sampling?

Ans:
· Joint sampling is done when there are equal number of events and non-events. Not appropriate for imbalanced data
· Separate sampling is done for imbalanced data. For rare event, all observations are kept when target = 1 and only few observations are kept when target = 0.

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