## False Negative

While understanding the hypothesis, two errors can be quite confusing. These two errors are false negative and false positive. You can also refer to...

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## False Negative

## Lorenz Curve

## Tokenization

## Multicollinearity

## Chi Test

## Log Loss

## Singular value decomposition

## Relu Activation Function

## Confounding Variable

## Confusion Matrix

While understanding the hypothesis, two errors can be quite confusing. These two errors are false negative and false positive. You can also refer to...

What Is a Lorenz Curve? An American economist, Max Lorenz, introduced the concept of the Lorenz curve in 1905. He proposed a graphical representation...

Tokenization is not a new concept, as people frequently use it in the blockchain. However, the concept of the token is older than blockchain....

With increasingly advanced machine learning and deep learning algorithms, you can solve almost any problem with proper datasets. However, as the complexity of the...

When developing a machine learning model, you may encounter numerous problems. One common problem related to feature selection determines how relevant the input features...

In machine learning, you can solve predictive modeling through classification problems. For each observation in the model, you must predict the class label. The...

Principal component analysis and singular value decomposition are among the two common concepts of linear algebra in machine learning. After collecting raw data, is...

Activate function is an essential element for designing a neural network. Choosing the activation function will give you complete control over the network model’s...

Confounding variable is a statistical term.The concept is a bit confusing for many people because of the method to use. For starters, different researchers...

The classification process helps with the categorization of the dataset into different classes. A machine learning model enables you to: Frame the problem, Collect...