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## Learn About Decision Tree Regression Algorithm in Python

In this video, you will learn about decision tree regression algorithm in python Other important playlists

#decisiontree  #regression  #python #machine-learning

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## Discovering Symbolic Models From Deep Learning With Inductive Biases

In machine learning, the aim is to create algorithms that can learn and predict a required target output from the learnings. To achieve this, the learning algorithm is presented and fed with some examples it can train and learn from to achieve the intended relation of input and output values.

#deep-learning #regression

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## Pros and Cons of popular Supervised Learning Algorithms

We all have used one of the following supervised learning algorithms for predictive analysis:

1. Logistic Regression
2. Ridge Regression
3. LASSO Regression
4. Linear Discriminant Analysis (LDA)
5. K Nearest Neighbors (KNN)
6. Naive Bayes (NB)
7. Support Vector Machine (SVM)
8. Decision Tree
9. Random Forest (RF)

But have you thought of their pros or cons? Here I have listed few :

• 1. Logistic Regression
• 2. Ridge Regression
• 3. LASSO Regression
• 4. Linear Discriminant Analysis (LDA)
• 5. K Nearest Neighbors (KNN)
• 6. Naive Bayes (NB)
• 7. Support Vector Machine (SVM)
• 8. Decision Tre
• 9. Random Forest (RF)

#classification #supervised-learning #regression #algorithms #machine-learning

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## A simple explanation of Machine Learning and Neural Networks

Deep learning, neural networks and machine learning have been the buzz words for the past few years. Surely, there is a lot that can be done using neural networks.

There has been immense research and innovation in the field of neural networks. Here are some amazing tasks that neural networks can do with extreme speed and good accuracy:

• Image classification — For example, when given images of cats and dogs, the neural network can tell which image has a cat and which has a dog.
• Detection — locating different objects in a given image.
• Chatbots — Neural networks can chat with humans in conversations. Neural networks can do tasks for humans based on the human’s typed request or spoken request.
• Language translation — Translating text from one language to another.
• Image generation — Generating new images of a particular type. The people in the picture below do not exist! These faces have been generated by AI!!

#linear-regression #machine-learning #deep-learning #regression #neural-networks

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## Types of Regression Algorithms

Regression Analysis is a statistical method for examining the relationship between two or more variables. There are many different types of regression analysis, of which a few algorithms will be considered below.

## 1. Linear Regression

Linear Regression models describe the relationship between a set of variables and a real value outcome. For example, the input of the mileage, engine size, and the number of cylinders of a car can be used to predict the price of the car using a regression model.

Regression differs from classification in how it’s error is defined. In classification, the predicted class is not the class in which the model is making an error. In regression, for example, if the actual price of a car is \$5000 and we have two models that predict the price to be \$4500 and \$6000, then we would prefer the former because it is less erroneous than \$6,000. We need to define a loss function for the model, such as Least Squares or Absolute Value.

The drawback of regression is that it assumes that a single straight line is appropriate as a summary of the data.

## 2. Polynomial Regression

Polynomial Regression is the same concept as linear regression except that it uses a curved line instead of a straight line (which is used by linear regression). Polynomial regression learns more parameters to draw a non-linear regression line. It is beneficial for data that cannot be summarized by a straight line.

The number of parameters (also called degrees) has to be determined. A higher degree model is more complex but can overfit the data.

## 3. Poisson Regression

Poisson Regression assumes that the predicted variables follow a Poisson Distribution. Hence, the values of the predicted variable are positive integers. The Poisson distribution assumes that the count of larger numbers is rare and smaller values are more frequent. Poisson regression is used for modeling rare occurrence events and count variables, such as incidents of cancer in a demographic or the number of times power shuts down at NASA.

#data-science #algorithms #programming #regression

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## Python Guide to Precision-Recall Tradeoff -

What do you think should we consider only the accuracy score as a benchmark for our classification task? Many beginners in this field have misunderstood; getting good accuracy for classification models means they have built a perfect model which classifies every instance. Well, you can consider only accuracy as a benchmark for regression problems.

#regression #python #tutorial

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## Xgboost regression training on CPU and GPU in python

### How to unlock the fast training of xgboost models in Python using a GPU

In this article, I want to go along with the steps that are needed to train xgboost models using a GPU and not the default CPU.

Additionally, an analysis of how the training speeds are influenced by the sizes of the matrices and certain hyperparameters is presented as well.

Feel free to clone or fork all the code from here: https://github.com/Eligijus112/xgboost-regression-gpu.

In order to train machine learning models on a GPU you need to have on your machine, well, a Graphical Processing Unit — GPU - a graphics card. By default, machine learning frameworks search for a Central Processing Unit — CPU — inside a computer.

#machine-learning #python #gpu #regression #cpu #xgboost regression training on cpu and gpu in python

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## Logistic Regression -Beginners Guide in Python - Analytics India Magazine

Most of the supervised learning problems in machine learning are classification problems. Classification is the task of assigning a data point with a suitable class. Suppose a pet classification problem. If we input certain features, the machine learning model will tell us whether the given features belong to a cat or a dog. Cat and dog are the two classes here. One may be numerically represented by 0 and the other by 1. This is specifically called a binary classification problem. If there are more than two classes, the problem is termed a multi-class classification problem. This machine learning task comes under supervised learning because both the features and corresponding class are provided as input to the model during training. During testing or production, the model predicts the class given the features of a data point.

This article discusses Logistic Regression and the math behind it with a practical example and Python codes. Logistic regression is one of the fundamental algorithms meant for classification. Logistic regression is meant exclusively for binary classification problems. Nevertheless, multi-class classification can also be performed with this algorithm with some modifications.

#developers corner #binary classification #classification #logistic regression #logit #python #regression #scikit learn #sklearn #statsmodels #tutorial

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## Lasso Regression Fundamentals and Modeling in Python

In this blog post, I will first try to explain the basics of Lasso Regression. Then, we’ll build the model using a dataset with Python. Finally, we’ll evaluate the model by calculating the mean square error. Let’s get started step by step.

## What is the Lasso Regression?

The main purpose in Lasso Regression is to find the coefficients that minimize the error sum of squares by applying a penalty to these coefficients. In another source, it is defined as follows:

_The “LASSO” stands for _L_east _A_bsolute _S_hrinkage and _S_election _Operator. Lasso regression is a regularization technique. It is used over regression methods for a more accurate prediction. This model uses shrinkage. Shrinkage is where data values are shrunk towards a central point as the mean. Lasso Regression uses L1 regularization technique. It is used when we have more number of features because it automatically performs feature selection.

### Features of Lasso Regression

• Ridge Regression’s all relevant-unrelated variables have been proposed to overcome the disadvantage of leaving the model.
• Lasso Regression brings the coefficients closer to zero.
• But when the norm L1 is large enough, it makes some coefficients zero. Thus, the variable makes the selection.
• It is very important that λ is chosen correctly. Cross-Validation is used for this.
• Ridge and Lasso methods are not superior to each other.

#algorithms #python #machine-learning #regression #data-science

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## Maximum Likelihood Estimation - Python Guide - Analytics India Magazine

Data is everywhere. The present human lifestyle relies heavily on data. Machine learning is a huge domain that strives hard continuously to make great things out of the largely available data. With data in hand, a machine learning algorithm tries to find the pattern or the distribution of that data. Machine learning algorithms are usually defined and derived in a pattern-specific or a distribution-specific manner. For instance, Logistic Regression is a traditional machine learning algorithm meant specifically for a binary classification problem. Linear Regression is a traditional machine learning algorithm meant for the data that is linearly distributed in a multi-dimensional space. One specific algorithm cannot be applied for a problem of different nature.

To this end, Maximum Likelihood Estimation, simply known as MLE, is a traditional probabilistic approach that can be applied to data belonging to any distribution, i.e., Normal, Poisson, Bernoulli, etc. With prior assumption or knowledge about the data distribution, Maximum Likelihood Estimation helps find the most likely-to-occur distribution parameters. For instance, let us say we have data that is assumed to be normally distributed, but we do not know its mean and standard deviation parameters. Maximum Likelihood Estimation iteratively searches the most likely mean and standard deviation that could have generated the distribution. Moreover, Maximum Likelihood Estimation can be applied to both regression and classification problems.

Therefore, Maximum Likelihood Estimation is simply an optimization algorithm that searches for the most suitable parameters. Since we know the data distribution a priori, the algorithm attempts iteratively to find its pattern. The approach is much generalized, so that it is important to devise a user-defined Python function that solves the particular machine learning problem.

#developers corner #likelihood #log likelihood #maximum likelihood estimation #mle #probability distribution #python #regression #statistics

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## Comprehensive Guide To Regression For Dummies - AIM

Regression is a set of statistical approaches used for approximating the relationship between a dependent variable and one or more independent variables. The term “regression” was coined by Francis Galton to describe the phenomenon of the heights of descendants of tall ancestors regressing down to the normal average, i.e., regression to mean. However, regression as a concept was created and employed by Legendre and Gauss, who used the least-squares method to determine the orbits of celestial bodies around the Sun.

#regression #tutorial

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## Multiple Regression As A Machine Learning Algorithm

Previously I wrote a couple of pieces on multivariate modeling but they both focused on time series forecasting. If curious, go ahead and check out the posts on vector autoregression and panel data modeling. Writing on multivariate regression (i.e. multiple linear regression) was always on my list but something else was on its way — I started a series on anomaly detection techniques! Once started that series, I could not stop until I wrote 11 consecutive posts.
Today I’m back with multiple regression and here’s the plan — first I’ll define in broad terms what a multiple regression is, and then list some real-world use cases as examples. The second part is going to be a rapid implementation of multiple regression in Python, just to give a broad intuition. In the third part, I’ll dive a bit deeper following the typical machine learning workflow. I’ll end with some additional points to keep in mind while implementing linear regression

#machine-learning #python #regression #scikit-learn #data-science

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## Linear Regression- Machine Learning

Linear regression is one of the easiest and most popular Machine Learning algorithms. It is a statistical method that is used for predictive analysis. Linear regression makes predictions for continuous/real or numeric variables such as sales, salary, age, product price, etc.
Linear regression algorithms show a linear relationship between a dependent (y) and one or more independent (y) variables, hence called linear regression. Since linear regression shows the linear relationship, which means it finds how the value of the dependent variable is changing according to the value of the independent variable.
When working with linear regression, our main goal is to find the best fit line that means the error between predicted values and actual values should be minimized. The best fit line will have the least error.
The different values for weights or the coefficient of lines (a0, a1) gives a different line of regression, so we need to calculate the best values for a0 and a1 to find the best fit line, so to calculate this we use cost function.

#regression #machine-learning-ai #data-science #linear-regression #machine-learning

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## Docker based Deployment of Machine Learning Model using Flask

Prerequisites

• Machine Learning — Simple Linear Regression
• Docker Setup
Overview
In a real-world setting, testing and training machine learning models is one phase of machine learning model development lifecycle. These models need to be deployed in real-world application to utilize it’s benefits. In this article I will discuss on how machine learning model can be deployed as a microservice in a plain Docker environment.
Now a days Kubernetes becoming more popular which act as a management layer for the Docker runtime. It provide necessary container management functionalities which suitable for production grade deployment. However choosing the deployment strategy is upto the organization that building the applications that utilize the machine learning models.