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Text Classification using machine learning

Text classification is one of the important task that can be done using machine learning algorithm, here in this blog post i am going to share how i started with the baseline model, then tried different models to improve the accuracy and finally settled down to the best model.

Micro F1 Score: In Micro-average method, you sum up the individual true positives, false positives, and false negatives of the system for different sets and the apply them to get the statistics.

Let’s start by importing all the required libraries Here i am using some sample data, you can use data from sklearn library Let’s split the data for training and testing purpose.

The score comparison below shows that micro and weighted F1 scores are generally similar for the same classifier, unlike macro F1 score.

F1 score

In statistical analysis of binary classification, the F1 score (also F-score or F-measure) is a measure of a test's accuracy.

It considers both the precision p and the recall r of the test to compute the score: p is the number of correct positive results divided by the number of all positive results returned by the classifier, and r is the number of correct positive results divided by the number of all relevant samples (all samples that should have been identified as positive).

The F1 score is the harmonic average of the precision and recall, where an F1 score reaches its best value at 1 (perfect precision and recall) and worst at 0.

The name F-measure is believed to be named after a different F function in Van Rijsbergen's book, when introduced to MUC-4.

[1] The traditional F-measure or balanced F-score (F1 score) is the harmonic mean of precision and recall: The general formula for positive real β is: The formula in terms of Type I and type II errors: Two other commonly used F measures are the

measure, which weighs recall higher than precision (by placing more emphasis on false negatives), and the

measure, which weighs recall lower than precision (by attenuating the influence of false negatives).

'measures the effectiveness of retrieval with respect to a user who attaches β times as much importance to recall as precision'.[2] It is based on Van Rijsbergen's effectiveness measure Their relationship is

The F-score is often used in the field of information retrieval for measuring search, document classification, and query classification performance.[4] Earlier works focused primarily on the F1 score, but with the proliferation of large scale search engines, performance goals changed to place more emphasis on either precision or recall[5] and so

The F-score is also used in machine learning.[6] Note, however, that the F-measures do not take the true negatives into account, and that measures such as the Matthews correlation coefficient, Informedness or Cohen's kappa may be preferable to assess the performance of a binary classifier.[3] The F-score has been widely used in the natural language processing literature, such as the evaluation of named entity recognition and word segmentation.

Optimal Thresholding of Classifiers to Maximize F1 Measure

In practice, decision rules that maximize F1 are often set empirically, rather than analytically.

That is, given a set of validation examples with predicted scores and true labels, rules for mapping scores to labels are selected that maximize F1 on the validation set.

In such situations, the optimal threshold can be subject to a winner’s curse [2] where a sub-optimal threshold is chosen because of sampling effects or limited training data.

This is because, for a fixed number of samples, some thresholds converge to their true error rates fast, while others have higher variance and may be set erroneously.

Each term in Equation (4) is the sum of O(n) i.i.d random variables and has exponential (in n) rate of convergence to the mean irrespective of the base rate b and the threshold t.

The threshold s that classifies all examples positive (and maximizes F1 analytically by Theorem 2) has an empirical F1 value close to its expectation of 2b1+b=21+1∕b since tp, fp and fn are all estimated from the entire data.

Despite the threshold s being far from optimal, it has a constant probability of having a better F1 value on validation data, a probability that does not decrease with n, for n <

(1 - b)/b2 the algorithm incorrectly selects large thresholds with constant probability, whereas for larger n it correctly selects small thresholds.

Figure 8 shows the result of simulating this phenomenon, executing 10,000 runs for each setting of the base rate, with n = 106 samples for each run used to set the threshold.

Accuracy, Recall and Precision

kobriendublin.wordpress.com Accuracy, Recall and Precision

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