Missing Features: A challenge in data preparation

Missing Features: A challenge in data preparation

What is Missing Features Problem? The principle of machine learning and AI based application is “Garbage in-garbage out”. Hence, data cleaning and Exploratory Data Analysis are the key steps of data preparation phase. In the real world, the data comes with noise. The noise may include many things such as incorrect data, unnecessary extra data, missing values, missing attributes/features. Among st them, it is difficult to handle missing features problem. Missing features is one of the most common problems that…

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Concept drift: A Challenge in predictive analytics

Concept drift: A Challenge in predictive analytics

What is concept drift? In predictive analytics, machine learning, and incremental learning the concept drift is an event that occurs when the statistical properties of the output parameter, which the model is trying to forecast, alters over time in unexpected behaviors. This results in glitches because the forecasts become less correct as time passes. In stream data mining, where input data arrives based on time instance this issue is prominent. In other words, online learning suffers most from concept drift problem. Let…

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Incremental Classification: Gaussian Naïve Bayes Algorithm with example

Incremental Classification: Gaussian Naïve Bayes Algorithm with example

Naïve Bayes algorithm Naïve Bayes is easy and effective technique for predictive modeling in machine learning. Incremental learning expects one at a time data instance while training. To achieve this in classification, simplest form of system is available in the literature of machine learning and statistics, i.e. Gaussian Naive Bayes. Before proceeding to read about this wonderful technique, make sure that you have read the basic concepts of machine learning and incremental learning as a pre-requisite. Naïve Bayes Algorithm: Introduction…

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All About Incremental Learning

All About Incremental Learning

Incremental learning is specialized category of machine learning techniques. It is relevant with time series analysis. Plain time series analysis statistical techniques have simple computational approaches, while in incremental learning one needs to implement specific algorithms to handle time component in the data and extract useful information/tend out of it. Statistical time series analysis techniques tend to cancel the non-stationary component of the data while incremental machine learning techniques handle non stationary data and hence are special. In this post,…

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Time Series Forecasting using ARIMA model

Time Series Forecasting using ARIMA model

ARIMA model: Introduction ARIMA is a widespread statistical technique for time series forecasting. It is an acronym. It stands for AutoRegressive Integrated Moving Average. This model captures a suite of diverse standard temporal structures in time series data.An ARIMA model is a class of statistical techniques for analyzing and forecasting time series data. To read more about time series analysis and its basic techniques please read this post. It clearly provides a suite of standard structures in time series data,…

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