Python: Independent Component Analysis (ICA) versus PCA

This post demonstrates the usage of Independent Component Analysis (ICA) in Python, a technique akin to PCA but focused on independent factors (source signals).

Understanding the LSTM model

This post gives a brief introduction to the basic structure of LSTM (Long-Short Term Memory) model with an intuitive figures and explanations. Let's interpret "gate" as "rate" or "weight" for grasping the concept of LSTM faster since we are not engineers but finance-related practitioners.

R code : Creating lagged Xs and y for supervised learning

This post shows a simple R code to create various lagged time series and concatenate them with the original time series. This can be used frequently when preprocessing time series data for machine/deep learning models.

R code : Back Transform from Caret's preProcess()

This post gives a small R code for the back transformation of the caret's preProcess() function, which is not implemented in caret R package yet.

Installing Python and Tensorflow with Jupyter Notebook Configurations

For a machine or deep learning modeling, Python is widely used with Tensorflow. This post explains the an installation of Python, Tensorflow and configuration of Jupyter notebook as a kickstart towards ML/DL modeling.

lambda.min, lambda.1se and Cross Validation in Lasso : Continuous Response

This post presents a R code for a k-fold cross validation of Lasso in the case of a gaussian regression (continuous Y). This work easily can be done by using a mean squared error. Except for this performance measure, the remianing procedures are the same as the ones utilized in the case of the binomial response.

lambda.min, lambda.1se and Cross Validation in Lasso : Binomial Response

This post explains more details regarding cross validation of Lasso in the case of a binomial response. We implement a R code for a lasso model's cross validation. In addition, We calculate lambda.min and lambda.1se manually. Finally we compare these two cross validation results with that of cv.glmnet().

Exclusive Lasso and Group Lasso using R code

This post shows how to use the R packages for estimating an exclusive lasso and a group lasso. These lasso variants have a given grouping order in common but differ in how this grouping constraint is functioning when a variable selection is performed.

Machine Learning : Workflow

This post gives a brief introduction to a workflow of machine learning model and mostly used R packages before diving into the details of these models.

Understanding Gradient Descent Algorithm with Python code

Gradient Descent (GD) is the basic optimization algorithm for machine learning or deep learning. This post explains the basic concept of gradient descent with python code.

Understanding Logistic Regression

This post explains the logistic regression and implements R code for the estimation of its parameters.

Sign Constrained Lasso with R code

This post explains how to implement the sign constrained lasso with ridge, and linear regression model. The restrictions of expected sign is of great importance in the case when building an econometric model with meaningful interpretation. We can easily incorporate sign restrictions to the above regression models using glmnet R package.

Lasso Regression Model with R code

Tibshirani (1996) introduces the so called LASSO (Least Absolute Shrinkage and Selection Operator) model for the selection and shrinkage of parameters. This model is very useful when we analyze big data. In this post, we learn how to set up the Lasso model and estimate it using glmnet R package.