11. What is inductive machine learning?
The inductive machine learning involves the process of learning by examples, where a system, from a set of observed instances, tries to induce a general rule.
12. What is the activation function in Machine Learning?
A function (for example, ReLU or sigmoid) that takes in the weighted sum of all of the inputs from the previous layer and then generates and passes an output value (typically nonlinear) to the next layer.
13. How does deductive and inductive machine learning differ?
Deductive machine learning starts with a conclusion, then learns by deducing what is right or wrong about that conclusion. Inductive machine learning starts with examples from which to draw conclusions.
14. How do you choose an algorithm for a classification problem?
The answer depends on the degree of accuracy needed and the size of the training set. If you have a small training set, you can use a low variance/high bias classifier. If your training set is large, you will want to choose a high variance/low bias classifier.
15. How do bias and variance play out in machine learning?
Both bias and variance are errors. Bias is an error due to flawed assumptions in the learning algorithm. Variance is an error resulting from too much complexity in the learning algorithm.
16. What is a class in machine learning?
One of a set of enumerated target values for a label. For example, in a binary classification model that detects spam, the two classes are spam and not spam. In a multi-class classification model that identifies dog breeds, the classes would be a poodle, beagle, pug, and so on.
17. What is the baseline for machine learning?
A simple model or heuristic used as reference point for comparing how well a model is performing. A baseline helps model developers quantify the minimal, expected performance on a particular problem.
18. What Is The Difference Between An Array And Linked List?
An array is an ordered fashion of collection of objects. A linked list is a series of objects that are processed in a sequential order.
19. What is a checkpoint in machine learning?
Data that captures the state of the variables of a model at a particular time. Checkpoints enable exporting model weights, as well as performing training across multiple sessions. Checkpoints also enable training to continue past errors (for example, job preemption). Note that the graph itself is not included in a checkpoint.
20. How To Handle Or Missing Data In A Dataset?
An individual can easily find missing or corrupted data in a data set either by dropping the rows or columns. On contrary, they can decide to replace the data with another value.
In Pandas they are two ways to identify the missing data, these two methods are very useful.
isnull() and dropna().