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Interpretability is one of the biggest challenges in machine learning. A model has more interpretability than another one if its decisions are easier for a human to comprehend. Some models are so complex and are internally structured in such a way that it’s almost impossible to understand how they reached their final results. These black boxes seem to break the association between raw data and final output, since several processes happen in between.
But in the universe of machine learning algorithms, some models are more transparent than others. Decision Trees are definitely one of them, and Linear Regression models are another one. Their simplicity and straightforward approach turns them into an ideal tool to approach different problems. Let’s see how.
You can use Linear Regression models to analyze how salaries in a given place depend on features like experience, level of education, role, city they work in, and so on. Similarly, you can analyze if real estate prices depend on factors such as their areas, numbers of bedrooms, or distances to the city center.
In this post, I’ll focus on Linear Regression models that examine the linear relationship between a **dependent variable **and one (Simple Linear Regression) or more (Multiple Linear Regression) independent variables.
#2020 oct tutorials # overviews #linear regression #python
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Let’s begin our journey with the truth — machines never learn. What a typical machine learning algorithm does is find a mathematical equation that, when applied to a given set of training data, produces a prediction that is very close to the actual output.
Why is this not learning? Because if you change the training data or environment even slightly, the algorithm will go haywire! Not how learning works in humans. If you learned to play a video game by looking straight at the screen, you would still be a good player if the screen is slightly tilted by someone, which would not be the case in ML algorithms.
However, most of the algorithms are so complex and intimidating that it gives our mere human intelligence the feel of actual learning, effectively hiding the underlying math within. There goes a dictum that if you can implement the algorithm, you know the algorithm. This saying is lost in the dense jungle of libraries and inbuilt modules which programming languages provide, reducing us to regular programmers calling an API and strengthening further this notion of a black box. Our quest will be to unravel the mysteries of this so-called ‘black box’ which magically produces accurate predictions, detects objects, diagnoses diseases and claims to surpass human intelligence one day.
We will start with one of the not-so-complex and easy to visualize algorithm in the ML paradigm — Linear Regression. The article is divided into the following sections:
Need for Linear Regression
Visualizing Linear Regression
Deriving the formula for weight matrix W
Using the formula and performing linear regression on a real world data set
Note: Knowledge on Linear Algebra, a little bit of Calculus and Matrices are a prerequisite to understanding this article
Also, a basic understanding of python, NumPy, and Matplotlib are a must.
Regression means predicting a real valued number from a given set of input variables. Eg. Predicting temperature based on month of the year, humidity, altitude above sea level, etc. Linear Regression would therefore mean predicting a real valued number that follows a linear trend. Linear regression is the first line of attack to discover correlations in our data.
Now, the first thing that comes to our mind when we hear the word linear is, a line.
Yes! In linear regression, we try to fit a line that best generalizes all the data points in the data set. By generalizing, we mean we try to fit a line that passes very close to all the data points.
But how do we ensure that this happens? To understand this, let’s visualize a 1-D Linear Regression. This is also called as Simple Linear Regression
#calculus #machine-learning #linear-regression-math #linear-regression #linear-regression-python #python
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Take your current understanding and skills on machine learning algorithms to the next level with this article. What is regression analysis in simple words? How is it applied in practice for real-world problems? And what is the possible snippet of codes in Python you can use for implementation regression algorithms for various objectives? Let’s forget about boring learning stuff and talk about science and the way it works.
#linear-regression-python #linear-regression #multivariate-regression #regression #python-programming
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Machine learning algorithms are not your regular algorithms that we may be used to because they are often described by a combination of some complex statistics and mathematics. Since it is very important to understand the background of any algorithm you want to implement, this could pose a challenge to people with a non-mathematical background as the maths can sap your motivation by slowing you down.
In this article, we would be discussing linear and logistic regression and some regression techniques assuming we all have heard or even learnt about the Linear model in Mathematics class at high school. Hopefully, at the end of the article, the concept would be clearer.
**Regression Analysis **is a statistical process for estimating the relationships between the dependent variables (say Y) and one or more independent variables or predictors (X). It explains the changes in the dependent variables with respect to changes in select predictors. Some major uses for regression analysis are in determining the strength of predictors, forecasting an effect, and trend forecasting. It finds the significant relationship between variables and the impact of predictors on dependent variables. In regression, we fit a curve/line (regression/best fit line) to the data points, such that the differences between the distances of data points from the curve/line are minimized.
#regression #machine-learning #beginner #logistic-regression #linear-regression #deep learning
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The most glamorous part of a data analytics project/report is, as many would agree, the one where the Machine Learning algorithms do their magic using the data. However, one of the most overlooked part of the process is the preprocessing of data.
A lot more significant effort is put into preparing the data to fit a model on rather than tuning the model to fit the data better. One such preprocessing technique that we intend to disentangle is Polynomial Regression.
#data-science #machine-learning #polynomial-regression #regression #linear-regression
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Generalized Linear Model (GLM) is popular because it can deal with a wide range of data with different response variable types (such as binomial_, Poisson, or _multinomial).Comparing to the non-linear models, such as the neural networks or tree-based models, the linear models may not be that powerful in terms of prediction. But the easiness in interpretation makes it still attractive, especially when we need to understand how each of the predictors is influencing the outcome.The shortcomings of GLM are as obvious as its advantages. The linear relationship may not always hold and it is really sensitive to outliers. Therefore, it’s not wise to fit a GLM without diagnosing.In this post, I am going to briefly talk about how to diagnose a generalized linear model. The implementation will be shown in R codes.There are mainly two types of diagnostic methods. One is outliers detection, and the other one is model assumptions checking.
Before diving into the diagnoses, we need to be familiar with several types of residuals because we will use them throughout the post. In the Gaussian linear model, the concept of residual is very straight forward which basically describes the difference between the predicted value (by the fitted model) and the data.
Response residuals
In the GLM, it is called “response” residuals, which is just a notation to be differentiated from other types of residuals.The variance of the response is no more constant in GLM, which leads us to make some modifications to the residuals.If we rescale the response residual by the standard error of the estimates, it becomes the Pearson residual.
#data-science #linear-models #model #regression #r