What is Time series?
A Time series is a sequence of numerical data points in successive order. It tracks the movement of the chosen data points, over a specified period of time and records the data points at regular intervals. Time series doesn’t require any minimum or maximum time input. Analysts often use Time series to examine data according to their specific requirement.
What is Kernel SVM?
SVM algorithms have basically advantages in terms of complexity. First I would like to clear that both Logistic regression as well as SVM can form non linear decision surfaces and can be coupled with the kernel trick. If Logistic regression can be coupled with kernel then why use SVM?
● SVM is found to have better performance practically in most cases.
● SVM is computationally cheaper O(N^2*K) where K is no of support vectors (support vectors are those points that lie on the class margin) where as logistic regression is O(N^3)
● Classifier in SVM depends only on a subset of points . Since we need to maximize distance between closest points of two classes (aka margin) we need to care about only a subset of points unlike logistic regression.
Which performance metric is better R2 or adjusted R2?
Adjusted R2 because the performance of predictors impacts it. R2 is independent of predictors and shows performance improvement through increase if the number of predictors is increased.
Why rotation is required in PCA? What will happen if you don’t rotate the components?
Rotation is a significant step in PCA as it maximizes the separation within the variance obtained by components. Due to this, the interpretation of components becomes easier.
The motive behind doing PCA is to choose fewer components that can explain the greatest variance in a dataset. When rotation is performed, the original coordinates of the points get changed. However, there is no change in the relative position of the components.
If the components are not rotated, then we need more extended components to describe the variance.
What is deep learning, and how does it contrast with other machine learning algorithms?
Deep learning is a subset of machine learning that is concerned with neural networks: how to use backpropagation and certain principles from neuroscience to more accurately model large sets of unlabelled or semi-structured data. In that sense, deep learning represents an unsupervised learning algorithm that learns representations of data through the use of neural nets.
How to deal with multicollinearity?
Multi collinearity can be dealt with by the following steps:
- Remove highly correlated predictors from the model.
- Use Partial Least Squares Regression (PLS) or Principal Components Analysis,
List the advantages and limitations of the Temporal Difference Learning Method.
Temporal Difference Learning Method is a mix of Monte Carlo method and Dynamic programming method. Some of the advantages of this method include:
- It can learn in every step online or offline.
- It can learn from a sequence which is not complete as well.
- It can work in continuous environments.
- It has lower variance compared to MC method and is more efficient than MC method.
Limitations of TD method are: - It is a biased estimation.
- It is more sensitive to initialization.
What are Kernels in SVM? List popular kernels used in SVM along with a scenario of their applications.
The function of kernel is to take data as input and transform it into the required form. A few popular Kernels used in SVM are as follows: RBF, Linear, Sigmoid, Polynomial, Hyperbolic, Laplace, etc.
How would you evaluate a logistic regression model?
A subsection of the question above. You have to demonstrate an understanding of what the typical goals of a logistic regression are (classification, prediction, etc.) and bring up a few examples and use cases.
What is the difference between artificial learning and machine learning?
Designing and developing algorithms according to the behaviours based on empirical data are known as Machine Learning. While artificial intelligence in addition to machine learning, it also covers other aspects like knowledge representation, natural language processing, planning, robotics etc.