# You asked: Can neural network work with dependent continuous variable?

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A neural net can , at least theoretically, approximate any continuous function. It is called the Universal approximation theorem. Of course it might still be hard to learn but in practice it generally works quite well even if you don’t find the optimal solution.

## Can we use neural network for continuous variable?

In a regression problem, the dependent variable is a continuous variable and independent variables can be continuous or categorical variables. Neural networks can also be trained to regression problems, so that they can be utilized latter for prediction purpose.

## Is neural network only for classification?

Neural networks can be used for either regression or classification. Under regression model a single value is outputted which may be mapped to a set of real numbers meaning that only one output neuron is required.

## Can neural networks be used for regression?

Neural networks are flexible and can be used for both classification and regression. … Regression helps in establishing a relationship between a dependent variable and one or more independent variables. Regression models work well only when the regression equation is a good fit for the data.

## Can you use categorical variables in neural network?

A categorical variable is a variable whose values take on the value of labels. … Machine learning algorithms and deep learning neural networks require that input and output variables are numbers. This means that categorical data must be encoded to numbers before we can use it to fit and evaluate a model.

## How can a neural network predict a continuous variable?

To predict a continuous value, you need to adjust your model (regardless whether it is Recurrent or Not) to the following conditions:

1. Use a linear activation function for the final layer.
2. Chose an appropriate cost function (square error loss is typically used to measure the error of predicting real values)

## Can I use CNN for regression?

Convolutional neural networks (CNNs, or ConvNets) are essential tools for deep learning, and are especially suited for analyzing image data. For example, you can use CNNs to classify images. To predict continuous data, such as angles and distances, you can include a regression layer at the end of the network.

## How does neural network classification work?

The Neural Network Algorithm on its own can be used to find one model that results in good classifications of the new data. … These methods work by creating multiple diverse classification models, by taking different samples of the original data set, and then combining their outputs.

## How do neural networks work?

Neural networks are computing systems with interconnected nodes that work much like neurons in the human brain. Using algorithms, they can recognize hidden patterns and correlations in raw data, cluster and classify it, and – over time – continuously learn and improve.

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## Why is neural network good for classification?

Neural networks help us cluster and classify. You can think of them as a clustering and classification layer on top of the data you store and manage. They help to group unlabeled data according to similarities among the example inputs, and they classify data when they have a labeled dataset to train on.

## Are neural networks classification or regression?

Classification refers to predictive modeling problems that involve predicting a class label or probability of class labels for a given input. For more on the difference between classification and regression, see the tutorial: Difference Between Classification and Regression in Machine Learning.

## Why neural network is better than linear regression?

Regression is method dealing with linear dependencies, neural networks can deal with nonlinearities. So if your data will have some nonlinear dependencies, neural networks should perform better than regression.

## How can neural networks improve regression?

Now we’ll check out the proven way to improve the performance(Speed and Accuracy both) of neural network models:

1. Increase hidden Layers. …
2. Change Activation function. …
3. Change Activation function in Output layer. …
4. Increase number of neurons. …
5. Weight initialization. …
6. More data. …
7. Normalizing/Scaling data.

## Can you use SVM with categorical variables?

Among the three classification methods, only Kernel Density Classification can handle the categorical variables in theory, while kNN and SVM are unable to be applied directly since they are based on the Euclidean distances.

## Does neural network require one hot encoding?

This type of encoding creates a new binary feature for each possible category and assigns a value of 1 to the feature of each sample that corresponds to its original category. One hot encoding is a highly essential part of the feature engineering process in training for learning techniques.

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## What kind of encoding techniques can you use for categorical variables?

This means that if your data contains categorical data, you must encode it to numbers before you can fit and evaluate a model. The two most popular techniques are an Ordinal Encoding and a One-Hot Encoding.

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