Think Logically
The Naive Bayes Classifier technique is based on the so-called Bayesian theorem and is particularly suited when the dimensionality of the inputs is high. Naive Bayes is a classi cation algorithm for binary and multiclass classi cation problems. The technique is easiest to understand when described using binary or categorical input values. It is called naive Bayes or …
Gradient Descent Gradient Descent is an optimization algorithm that optimize the cost of the function.The goal is to continue to try different values for the coefficients, evaluate their cost and select new coefficients that have a slightly better (lower) cost. https://www.hackerearth.com/blog/machine-learning/3-types-gradient-descent-algorithms-small-large-data-sets/ https://medium.com/@zhaoyi0113/python-implementation-of-batch-gradient-descent-379fa19eb428 https://www.analyticsvidhya.com/blog/2017/03/introduction-to-gradient-descent-algorithm-along-its-variants/
In Machine Learning we use few mathematical terms that are important and frequently used, with this article I tried to list down those important keywords. When we walk on ML, we need a clear visualization of these terms so that we can understand what we want to achieve by using the algorithms. Machine learning is …
The multi-class classification is to assign an instance to one of the sets of classes. scikit-learn uses a strategy called one-vs.-all, or one-vs.-the-rest, to support multi-class classification. The goal of multi-class classification is to assign an instance to one of the sets of classes We can download the data from https://www.kaggle.com/c/sentiment-analysis-on-movie-reviews/data, the use following steps: Import the …
A Classical way, Logistic Regression is the younger son of Mr.ML, he is very efficient in predicting any problem associated with binary values. Whenever any person comes to Mr.ML and having problems like will his loan approves or not?, is it possible that he gets profit this year or not?, usually he asks his son Logistic …
In order to calculate score of different types of Algorithm we use following types of methods, few methods from SkLearn library are mentioned below. Scoring Function Comment Classification ‘accuracy’ metrics.accuracy_score ‘average_precision’ metrics.average_precision_score ‘f1’ metrics.f1_score for binary targets ‘f1_micro’ metrics.f1_score micro-averaged ‘f1_macro’ metrics.f1_score macro-averaged ‘f1_weighted’ metrics.f1_score weighted average ‘f1_samples’ metrics.f1_score by multilabel sample ‘neg_log_loss’ metrics.log_loss …