Data Science is a combination of algorithms, tools, and machine learning technique which helps you to find common hidden patterns from the given raw data.
What is logistic regression in Data Science?
Logistic Regression is also called as the logit model. It is a method to forecast the binary outcome from a linear combination of predictor variables.
Discuss Decision Tree algorithm
A decision tree is a popular supervised machine learning algorithm. It is mainly used for Regression and Classification. It allows breaks down a dataset into smaller subsets. The decision tree can able to handle both categorical and numerical data.
What is Prior probability and likelihood?
Prior probability is the proportion of the dependent variable in the data set while the likelihood is the probability of classifying a given observant in the presence of some other variable.
Explain Recommender Systems?
It is a subclass of information filtering techniques. It helps you to predict the preferences or ratings which users likely to give to a product.
Name three disadvantages of using a linear model
Three disadvantages of the linear model are:
The assumption of linearity of the errors.
You can't use this model for binary or count outcomes
There are plenty of overfitting problems that it can't solve
List out the libraries in Python used for Data Analysis and Scientific Computations.
The power analysis is an integral part of the experimental design. It helps you to determine the sample size requires to find out the effect of a given size from a cause with a specific level of assurance. It also allows you to deploy a particular probability in a sample size constraint.
Explain Collaborative filtering
Collaborative filtering used to search for correct patterns by collaborating viewpoints, multiple data sources, and various agents.
Discuss 'Naive' in a Naive Bayes algorithm?
Bias is an error introduced in your model because of the oversimplification of a machine learning algorithm." It can lead to underfitting.
What is a Linear Regression?
The Naive Bayes Algorithm model is based on the Bayes Theorem. It describes the probability of an event. It is based on prior knowledge of conditions which might be related to that specific event.
What is Linear regression?
Linear regression is a statistical programming method where the score of a variable 'A' is predicted from the score of a second variable 'B'. B is referred to as the predictor variable and A as the criterion variable.
State the difference between the expected value and mean value
They are not many differences, but both of these terms are used in different contexts. Mean value is generally referred to when you are discussing a probability distribution whereas expected value is referred to in the context of a random variable.
What the aim of conducting A/B Testing?
AB testing used to conduct random experiments with two variables, A and B. The goal of this testing method is to find out changes to a web page to maximize or increase the outcome of a strategy.
Define the term cross-validation
Cross-validation is a validation technique for evaluating how the outcomes of statistical analysis will generalize for an Independent dataset. This method is used in backgrounds where the objective is forecast, and one needs to estimate how accurately a model will accomplish.
Discuss Artificial Neural Networks
Artificial Neural networks (ANN) are a special set of algorithms that have revolutionized machine learning. It helps you to adapt according to changing input. So the network generates the best possible result without redesigning the output criteria.
How can you iterate over a list and also retrieve element indices at the same time?
This can be done using the enumerate function which takes every element in a sequence just like in a list and adds its location just before it.
Can you use machine learning for time series analysis?
Yes, it can be used but it depends on the applications.
What is the importance of having a selection bias?
Selection Bias occurs when there is no appropriate randomization acheived while selecting individuals, groups or data to be analysed.Selection bias implies that the obtained sample does not exactly represent the population that was actually intended to be analyzed.Selection bias consists of Sampling Bias, Data, Attribute and Time Interval.
What are the basic assumptions to be made for linear regression?
Normality of error distribution, statistical independence of errors, linearity and additivity.