Data Science

False positive paradox

A false positive is an error when test results incorrectly indicate presence of a condition when it doesn’t exist. False positives often plays an important role in hypothesis...

Data Science

Loss functions

In machine learning, the difference between the predicted output and the actual output is used to tune the parameters of the algorithm. This error in prediction, so called...

Data Science

Optimizers

Building neural networks, we compute gradients with the backpropagation algorithm. These gradients are used to perform the parameter updates. The default way of doing this is to update...

Data Science

Methods of Hyperparameter optimization

The parameters, called hyperparameters, that define the performance of the machine learning algorithm (model), depends on the problem we are trying to solve. Thus, they need to be...

Data Science

The Bayesian Thinking - III

Read the first part The Bayesian Thinking - I and the second part The Bayesian Thinking - II.

Data Science

The Bayesian Thinking - II

Statistics is the study of uncertainity. One way to deal with uncertainity is by probabilities.

Data Science

The Bayesian Thinking - I

A disease has affected 0.1% of the world’s population. The test for the disease correctly identifies 99% of people who have the disease and only incorrectly identifies 1%...

Data Science

Dropout: Prevent overfitting

Dropout is a regularization technique that prevents neural networks from overfitting. Regularization methods like L2 and L1 reduce overfitting by modifying the cost function. Dropout, on the other...

Data Science

How deep should neural nets be?

How do we decide on what architecture to use while solving a problem using neural networks? Should we use no hidden layers? One hidden layer? Two hidden layers?...

Data Science

Don't use sigmoid: Neural Nets

In neural networks, activation functions are used to introduce non-linearity in the model. There are several activation functions to choose from. Traditionally, people have been using sigmoid as...

Data Science

Scaling vs Normalization

Feature scaling (also known as data normalization) is the method used to standardize the range of features of data. Since, the range of values of data may vary...

Data Science

Ensembling is the key

Most of us have our favourite machine learning algorithms. For some, it may be state-of-the-art algos like Support Vector Machines while for others it may be something simple...

Data Science

Computational graphs: Backpropagation

Backpropagation is an efficient method of computing gradients in directed graphs of computations, such as neural networks. This is not a learning method, but rather a nice computational...

Data Science

Gradient descent: The core of neural networks

As discussed in the post linear algebra and deep learning, the optimization is the third and last step in solving image classification problem in deep learning. It helps...

Data Science

Gradient boosted trees: Better than random forest?

Does gradient boosted trees generally perform better than random forest? Let’s see that. But, first what are these methods? Random forest and boosting are ensemble methods, proved to...

Data Science

Linear algebra: The essence behind deep learning

Mathematics lies behind every algorithm; if not mathematics then mathematical thinking. In case of deep learning algorithms, linear algebra is the driving force.

Data Science

Data Mining: Knowledge discovery in databases

Knowledge discovery in databases (KDD) is a 7 step process to search for hidden knowledge in data. Data Mining refers to the analysis step in the KDD process....

Data Science

Anscombe's Quartet

We often look for summary statistics during EDA (Exploratory Data Analysis). But, sometimes these statistics may give us wrong interpretation of the data. In 1973, a statistician Francis...

Data Science

The Curse of Dimensionality

While applying k nearest neighbors approach in solving a problem, we can sometimes notice that there is a deterioration in the kNN performance when the number of predictors,...

Data Science

Dealing with categorical data

Often in our machine learning model, we encounter qualitative predictors.

Data Science

Regularization

Our machine learning model often encouters the problem of overfitting. Regularization is one of the techniques to solve this problem.

Data Science

Evaluation metrics for classification and False positives

A false positive error or false positive (false alarm) is a result that indicates a given condition exists when it doesn’t.

Data Science

Simplicity doesn't imply accuracy

Often, people say things like beauty lies in simplicity, simplicity is the glory of expression, complexity is the enemy of execution. But to what extent, these statements are...

Data Science

p-Value

In data science, p-value is used to determine statistical significance of the result. It gives the probability of a statistical model that, when the null hypothesis is true,...

Data Science

Out-liars

An outlier in our data can sometimes adversely affect our machine learning model. An outlier is any value that is distant from other observations in our data.

Data Science

Correlation is not causation

We often calculate correlation during EDA (Exploratory data analysis) to check how strongly two variables are correlated to one another. It’s tempting to assume that one variable causes...

Data Science

Overfitting and Underfitting

In machine learning, sometimes the prediction of our model may not be satisfactory. Although there may be many reasons for that, often it is due to either overfitting...

Data Science

Data leakage: A big problem

Let’s say your machine learning model performs better than than you expect it to in the test set. You are happy. Well, you should be. Now, you release...

Data Science

Simpson's paradox

In 1973, the University of California, Berkeley was sued for gender bias against women who had applied to graduate schools. The data for fall 1973 showed that men...

Data Science

Email spam filtering: Text analysis in R

Email spam1, also known as junk email, is a type of electronic spam where unsolicited messages are sent by email.

Data Science

Friendship paradox: facebook

Data Science

Moneyball: Why no prediction can't be made for baseball champion

Data Science

Moneyball: How linear regression changed baseball

It’s unbelievable how much you don’t know about the game you’ve been playing all your life. — Mickey Mantle