# An Overview of Linear Regression Models

**regression**problem. The simplest model of such a relationship can be described by a linear function - referred as

*linear regression*.

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Skip to main content# An Overview of Linear Regression Models

# A Practical guide to Autoencoders

# Understanding Boosted Trees Models

# A Practical Guide to Tree Based Learning Algorithms

# Understanding Support Vector Machine via Examples

# Support Vector Machines

# EDA of Lending Club Data - II

# EDA of Lending Club Data

## SO WHAT DO YOU THINK ?

Modeling the relationship between a scalar response (or dependent variable)
and one or more explanatory variables (or independent variables) is commonly
referred as a **regression** problem. The simplest model of such a
relationship can be described by a linear function - referred
as *linear regression*.

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Usually in a conventional neural network, one tries to predict a target vector
$y$ from input vectors $x$. In an auto-encoder network, one tries to predict
$x$ from $x$. It is trivial to learn a mapping from $x$ to $x$ if the network
has no constraints, but if the network is constrained the learning process
becomes more interesting. In this article, we are going to take a detailed
look at the mathematics of different types of autoencoders (with different
constraints) along with a sample implementation of it using Keras,
with a tensorflow back-end.

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In the previous post, we learned about tree based learning methods - basics of tree based models and the use of bagging to reduce variance. We also looked at one of the most famous learning algorithms based on the idea of bagging- random forests. In this post, we will look into the details of yet another type of tree-based learning algorithms: boosted trees.

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Tree based learning algorithms are quite common in data science competitions.
These algorithms empower predictive models with high accuracy, stability and ease of
interpretation. Unlike linear models, they map non-linear relationships
quite well. Common examples of tree based models are:
decision trees,
random forest, and
boosted trees.

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In the previous post on Support Vector Machines (SVM), we looked at the mathematical details of the algorithm. In this post, I will be discussing the practical implementations of SVM for classification as well as regression.
I will be using the iris dataset as an example for the classification problem, and a randomly generated data as an example for the regression problem.

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In this post we will explore a class of machine learning methods called
Support Vector
Machines also
known commonly as *SVM*.

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In the last post we looked at some initial cleanup of the data. We will start from there by loading the pickled dataframe.

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We will first look at various aspects of the LendingClub data using techniques of Exploratory Data Analysis (EDA).
Please look at my past post for finding further details on EDA techniques.
Different data files for this analysis have already been downloaded in the current folder.

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