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### Hidden Markov Models Algorithms

BySakshi

A hidden Markov model (HMM) is one in which you observe a sequence of emissions, but do not know the sequence of states the model went through to generate the emissions. We analyze the hidden Markov models to recover the sequence of states from the observed data. You can read more about it from our earlier…

### 4) Cross-validation to reduce Overfitting

Cross-validation (CV) is part 4 of our article on how to reduce overfitting. Its one of the techniques used to test the effectiveness of a machine learning model, it is also a resampling procedure used to evaluate a model if we have limited data. To perform CV we need to keep aside a sample/portion of the data which…

### Confusion Matrix for Model Selection

Before we jump into calculating Accuracy, Precision, and Recall for our classification model, we first need to understand what a Confusion matrix is. In machine learning, Classification is used to split data into categories. But after cleaning and preprocessing the data and training our model, how do we know if our classification model performs well?…

### Gradient Descent (now with a little bit of scary maths)

Buckle up Buckaroo because Gradient Descent is gonna be a long one (and a tricky one too). The whole article would be a lot more “mathy” than most articles as it tries to cover the concepts behind a Machine Learning algorithm called Linear Regression. If you don’t know what Linear Regression is, go through this article once. It would help…

### Model Selection using R-squared (R²) Measure

If you are looking for a widely-used measure that describes how powerful a regression is, the R-squared will be your cup of tea. R² tells you how related two things are. However, we tend to use R² because it’s easier to interpret. R² is the percentage of variation (i.e. varies from 0 to 1) explained by the relationship between two…

### 2. Ensemble Learning Methods

“Unity is strength”. This old saying expresses pretty well the underlying idea that rules the very powerful “ensemble methods” . Ensemble methods* are techniques that combine the decisions from several base machine learning (ML) models to find a predictive model to achieve optimum results. This algorithm can be any machine learning algorithm such as logistic…