Think Logically
A Classical way, Logistic Regression is the younger son of Mr.ML, he is very efficient in predicting any problem associated with binary values. Whenever any person comes to Mr.ML and having problems like will his loan approves or not?, is it possible that he gets profit this year or not?, usually he asks his son Logistic …
Today, I am going to tell you a story of a Philosopher, Mr.Mathematics. He is a philosopher of calculation. He is as old as the sun or the moon. With his philosophy, we measure distance, predict the basics of time. His wife is Lady Calculation. She is intellectual, smart and honest. Now I know you …
Python supports a concept called “list comprehensions”. It can be used to construct lists in a very natural, easy way, like a mathematician is used to do. List comprehension is a complete substitute for the lambda function as well as the functions map(), filter() and reduce().Usually we write mathematical collection of prime numbers, even and odd …
Perceptron an artificial Neuron A brain is composed of cells called neurons that process information and connections between neurons called synapses through which information is transmitted. It is estimated that the human brain is approximately composed of 100 billion neurons and 100 trillion synapses. The following image, represents the main components of a neuron are dendrites, …
Difference between Soft and Hard Computing ? Hard computing based on binary logic, crisp systems, numerical analysis and crisp software. It can deal only with exact data. Soft computing based on fuzzy logic, neural nets and probabilistic reasoning i.e it is tolerant of imprecision, uncertainty, partial truth, and approximation. It may deal with noisy data. …
In order to calculate score of different types of Algorithm we use following types of methods, few methods from SkLearn library are mentioned below. Scoring Function Comment Classification ‘accuracy’ metrics.accuracy_score ‘average_precision’ metrics.average_precision_score ‘f1’ metrics.f1_score for binary targets ‘f1_micro’ metrics.f1_score micro-averaged ‘f1_macro’ metrics.f1_score macro-averaged ‘f1_weighted’ metrics.f1_score weighted average ‘f1_samples’ metrics.f1_score by multilabel sample ‘neg_log_loss’ metrics.log_loss …