Naive Data

Photo by  Arseny Togulev This is the second part of the series on the Basics of Machine Learning. Please refer to this  link  for part 1. Assumptions of Simple Linear Regression Below are the main assumptions of linear regression: There is a  linear  relationship between X and Y. Error terms are  normally  distributed. Error terms are  independent  of each other. Change in one error term should not impact the other error terms. Error terms have  constant variance  i.e. variance shouldn’t increase or decrease as the error values change. Also, they shouldn’t follow any pattern. If the variance is not constant or homoscedastic then the inferences made on the model would be unreliable. Checking the Model Fit: We should always make sure that the model fit is really a good fit and is not by a chance. There are many ways to test it. T-Test: The t-distribution is also a kind of normal distribution; it is also symmetric and single-peaked, bu...

Photo by Franck V.   In this  post series we are going to learn the basics of Machine Learning. Let's starts with what is Machine Learning: “Machine Learning is the science of getting computers to learn and act like humans do, and improve their learning over time in autonomous fashion, by feeding them data and information in the form of observations and real-world interactions.”  Below are the top practical definitions from different sources: “Machine Learning at its most basic is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world.” – Nvidia  “Machine learning is the science of getting computers to act without being explicitly programmed.” – Stanford “Machine learning is based on algorithms that can learn from data without relying on rules-based programming.”- McKinsey & Co. “Machine learning algorithms can figure out how to perform important tasks by generalizing from examples.”...

Linear regression is one of the widely used statistical techniques for analysis. It provides a relationship between the target and the predictors. The below graph shows the linear relationship between the “dependent” variable Y, whose values we wish to predict and the “independent” variable X from which we wish to predict it. There are two types of linear regression: 1. Simple Linear Regression : It denotes the relationship between one variable and the target variable. The above graph represents the Simple Linear Regression. 2. Multiple Linear Regression :     It denotes the relationship between 2 or more independent variables and the corresponding dependent variable. The independent variables can be continuous or categorical. The below graph shows the multiple linear regression. Properties of Linear Regression Below are the main properties of linear regression: There is a linear relationship between X a...

  Photo by  Jens Lelie  on  Unsplash This is the second post in In Bytes series. Refer this link to read about the first post. Random Forest: Random forest is an ensemble made by the combination of a large number of decision trees. Ensemble: An ensemble means a group of things viewed as a whole rather than individually. In ensembles, a collection of models is used to make predictions, rather than individual models. In principle, ensembles can be made by combining all types of models. An ensemble can have a logistic regression, a neural network, and few decision trees working in unison. While choosing the model , we need to check for two things- Diversity and Acceptability. Diversity ensures that the models serve complementary purposes, which means that the individual models make predictions independent of each other. The advantages of this are different depending on the type of ensemble. Diversity ensures that even if some trees overfit, the other trees in the ensemb...