Grid Search Tuning of Hyper Parameters in Random Forest Classifier –
The Classification is a text mining tasks in which class of a particular input is identified by using a given set of labelled data. Both supervised and unsupervised methods are used for classification. In the first method, learning is done through predefined labelled data. In this, a set of labelled input documents are given to the model by the end-user. The two main categories of supervised learning are parametric and non-parametric classification. The probability distribution of each class is the base of parametric classification. If the density function is known, it will be better to use nonparametric classification. Recently, people are using this classification process especially supervised classification to develop multiple interesting platforms for business. Sentiment analysis is the most attractive platforms which make use of the advantages of supervised classification methods.
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Sentiment can be described as a person’s feeling about a particular thing. It includes the task of binary classification in which documents are classified into two different classes such as positive sentiment or negative sentiment. Due to the fast popularity of social networks sharing their views, opinion and ideas. Social networks provide a platform for the people to create a virtual civilization. Sentiment analysis is a mining process based on user-generated comments to identify positive or negative feelings. Opinions are always important to a business. Most of the business decision is performed based on customers’ reviews. The analysis of customer or product review involves the extraction of sentiment from product document. Business organizations are very conscious to know whether customers like their product or service, what customers feel about the product, which type of product or service customers like or dislike. Sentiment analysis is usually applied text input which help to identify the sentiment in a particular document and thus it is considered as the main part of text mining. Other than text classification, it requires more knowledge of the language. Generally, machine learning algorithms are considering the occurrence of the words in a document, so it tough to recognize the supreme attitude in that specific document. The sentiment analysis should be the process of identifying the polarity present in the given text or document i.e., positive or negative. There are number of supervised machine learning algorithms are used for sentiment analysis. The performance of these classification algorithm is depending on its specific domain. Random Forest classifier is largely used for this purpose. It is considered as an ensemble method. which generates many classifiers and finally aggregates their result for prediction. This will create a number of decision trees in the training phase. The risk of noise and outliers will be high when having a single tree in classifier and it will definitely reduce the output of the processing. Due to the randomness property of Random Forest classifier, it is highly robust to outliers and noises. This classifier can handle missing values also. One better approach to increase the outcome of any classifier is to tune the hyper parameters of that classifier. The parameters that are set by the data analysts before the training process is called hyper parameters.
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In this work, the Grid Search approach is applied for tuning Random Forest classifier and tried to identify the best hyper parameters. The implementation of Grid Search is simple. A set of hyper parameters and their values are feed to it first and then run an exhaustive search overall all possible combination of given values then training the model for each set of values. Then Grid Search algorithm will compare the score of each model it trains and keeps the best one. A common extension of Grid Search is to use cross-validation i.e., training the model on several different folds with different hyper parameter combinations to find more accurate results. Hyper parameter tuning- grid search method: Machine learning model has many parameters to tune and by tweaking these parameters, the performance of the model can improve. Hyper parameter tuning is the best method to execute a different number of parameter combinations to assess a classifier’s performance. Assessing a classifier by using training data will cause a fundamental machine learning problem called overfitting. The overfitting is the situation in which a model performs poorly on test data and highly on raining data. Therefore, cross-validation is used with the grid search method for hyper parameter optimization.
The grid search method is an approach used to identify the optimum parameters of a classifier so that a model can accurately predict some unlabelled data. The Grid Search method is used to tune some hyper parameters which cannot directly learn from the training process. The classification model has many hyper parameters and finding the best combination of these parameters is a challenging process. One of the best methods used for this purpose is the Grid Search method. Suppose, a machine learning model X has hyper parameters h1, h2 and h3. The Grid Search method defines a range of values for each hyper parameter h1, h2 and h3. It will construct many versions of X with all possible combinations of h1, h2 and h3. This range of hyper parameter values is known as a grid.
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Author:-
Suraj Kale
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