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Online Learning in Big Data
- Online Learning is assistant field of machine learning
- It permits to extend supervised learning model into a enormous dataset
- The general idea behind this is , It s is enough to study every instance at a time, it’s not required to study all the values in memory to fix a model
- The way to deploy an online learning algorithm using logistic regression is projected here. In majority of the supervised learning algorithms, it reduces the cost function.
- The cost function in logistic regression is noted as follows
- J(θ) denotes the cost function and hθ (x) denotes the assumption. The logistic regression is represented using the notation
- The cost function is explored, next is to identify an algorithm to reduce it
- The non difficult algorithm to attain this is termed as stochastic gradient descent
- The revised protocol of the algorithm for the weights of the logistical model of regression is represented as
- Here, From a kaggle competition , the titanic dataset is taken to work
- The source data is available in the directory bda/part3/vwfolder
- csv is a training data
- csv is an unnamed data to create new forecasts
- csv format is to be converted into vowpal wabbit input format
- Utilize csv_to_vowpal_wabbit.py python script
- It needs python t work on. It lies in the forlder bda/part3/vwfolder
- In a terminal , run the command : python csv_to_vowpal_wabbit.py
![Online Learning online learning img1](https://www.assignmenthelp.net/webimg/ahp/big-data-assignment-help/online-learning-img1.jpg)
![Online Learning online learning img2](https://www.assignmenthelp.net/webimg/ahp/big-data-assignment-help/online-learning-img2.jpg)
![Online Learning online learning img3](https://www.assignmenthelp.net/webimg/ahp/big-data-assignment-help/online-learning-img3.jpg)
The underlying code displays the regression model result while executing in a command line. By the result, it is attained with an average log loss and a report about the performance of an algorithm
![Online Learning online learning img4](https://www.assignmenthelp.net/webimg/ahp/big-data-assignment-help/online-learning-img4.jpg)
![Online Learning online learning img5](https://www.assignmenthelp.net/webimg/ahp/big-data-assignment-help/online-learning-img5.jpg)
The model.vw is employed and trained to produce the forecast with the newly available data
![Online Learning online learning img6](https://www.assignmenthelp.net/webimg/ahp/big-data-assignment-help/online-learning-img6.jpg)
The forecasts from the above command are unnormalized to fix in the range of 0 to 1. To perform this, sigmoid transformation is employed
![Online Learning online learning img7](https://www.assignmenthelp.net/webimg/ahp/big-data-assignment-help/online-learning-img7.jpg)
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