The Power of Decision Stumps
A decision stump is the weak classification model with the simple tree structure consisting of one split, which can also be considered a one-level decision tree. Due to its simplicity,...continue reading.
Category: Machine learning
Wind Resource Assessment
This is an article we recently published on “Renewable and Sustainable Energy Reviews”. It starts with a thorough review of the methods used for wind resource assessment: from algorithms based...continue reading.
Evaluation of time series forecasting using Spark windowing
Evaluation metrics play a critical role in machine learning ecosystem. Especially for machine learning products, evaluation metrics are like the heart beats. They show how healthy the model is and...continue reading.
Evaluation of time series forecasting using Spark windowing
Evaluation metrics play a critical role in machine learning ecosystem. Especially for machine learning products, evaluation metrics are like the heart beats. They show how healthy the model is and...continue reading.
Shiny 1: Amazon AWS for R and Shiny
Learn how to set up an Amazon AWS Ubuntu instance on which you can install R , RStudio, OpenCPU, or Shiny Server. The post Shiny 1: Amazon AWS for R...continue reading.
hat tip: join two spark dataframe on multiple columns (pyspark)
Consider the following two spark dataframes:df1.show()+—-+——+——-+|id_a|time_a|value_a|+—-+——+——-+| 1| 1| CA|| 1| 2| CA|| 2| 1| TX|| 3| 5| NE|| 4| 6| WA|+—-+——+——-+df2.show(…continue reading.
Ensemble Learning with Cubist Model
The tree-based Cubist model can be easily used to develop an ensemble classifier with a scheme called “committees”. The concept of “committees” is similar to the one of “boosting” by...continue reading.
Model Segmentation with Cubist
Cubist is a tree-based model with a OLS regression attached to each terminal node and is somewhat similar to mob() function in the Party package (https://statcompute.wordpress.com/2014/10/26/model-segmentation-with-recursive-partitioning). Below is a demonstrate...continue reading.
A Grid Search for The Optimal Setting in Feed-Forward Neural Networks
The feed-forward neural network is a very powerful classification model in the machine learning content. Since the goodness-of-fit of a neural network is majorly dominated by the model complexity, it...continue reading.
PART – A Rule-Learning Algorithm
> require(‘RWeka’) > require(‘pROC’) > > # SEPARATE DATA INTO TRAINING AND TESTING SETS > df1 <- read.csv(‘credit_count.csv’) > df2 <- df1[df1$CARDHLDR == 1, 2:12] > set.seed(2013) > rows <-...continue reading.
Generalized Boosted Regression with A Monotonic Marginal Effect for Each Predictor
In the practice of risk modeling, it is sometimes mandatory to maintain a monotonic relationship between the response and each predictor. Below is a demonstration showing how to develop a...continue reading.
Exchange Data between Python and R with SQLite
SQLite is a light-weight database with zero-configuration. Being fast, reliable, and simple, SQLite is a good choice to store / query large data, e.g. terabytes, and is well supported by...continue reading.
Portfolio Trading
In finance and investing the term portfolio refers to the collection of assets one owns. Compared to just holding a single asset at a time a portfolio has a number...continue reading.
Fit and Visualize A MARS Model
################################################# ## FIT A MULTIVARIATE ADAPTIVE REGRESSION ## ## SPLINES MODEL (MARS) USING MDA PACKAGE ## ## DEVELOPED BY HASTIE AND TIBSHIRANI ## ##############################################…continue reading.
E-Learning to Machine Learn
machine learningA quick post about online educational resources on machine learning. Perhaps its a sign of increasing popularity of the field that there are now several courses on machine learning...continue reading.
Learning Kernels SVM
Machine Learning and Kernels A common application of machine learning (ML) is the learning and classification of a set of raw data features by a ML algorithm or technique. In...continue reading.
