# Why neural networks are preferred over logistic model for solving classification problem?

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Compared to logistic regression, neural network models are more flexible, and thus more susceptible to overfitting. Network size can be restricted by decreasing the number of variables and hidden neurons, and by pruning the network after training.

## Which is better neural network or logistic regression?

Results. ANN significantly outperformed logistic models in both fields of discrimination and calibration but under performed in accuracy. In 77.8% of cases the area under the ROC curves and in 56.4% of cases the HL statistics for the neural network model were superior to that for the logistic model.

## Why are neural networks 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.

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## Can a problem based on logistic regression be solved using neural networks?

3) True-False: Is it possible to design a logistic regression algorithm using a Neural Network Algorithm? True, Neural network is a is a universal approximator so it can implement linear regression algorithm.

## Why are neural networks better than 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 is a neural network similar to logistic regression?

Artificial neural networks have inputs and outputs, just like logistic regression, but have one or more additional layers called hidden layers comprised of hidden units. Hidden layers can contain any number of hidden units.

## What is the relation between logistic regression and neural network?

To recap, Logistic regression is a binary classification method. It can be modelled as a function that can take in any number of inputs and constrain the output to be between 0 and 1. This means, we can think of Logistic Regression as a one-layer neural network.

## Why does neural network work so well?

Neural Networks can have a large number of free parameters (the weights and biases between interconnected units) and this gives them the flexibility to fit highly complex data (when trained correctly) that other models are too simple to fit.

## How effective are neural networks?

Recent neural networks have been able to accurately identify over 99.5% of the validation examples correctly (Chang and Chen, 2016). However, MNIST is non-trivial, as these excellent results were only achieved in recent years using deep learning.

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## How are neural networks used in classification?

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.

## Why is DNN better than logistic regression?

A neural network is more complex than logistic regression. … In practice, a neural network model for binary classification can be worse than a logistic regression model because neural networks are more difficult to train and are more prone to overfitting than logistic regression.

## How does logistic regression differ from Perceptron?

Originally a perceptron was only referring to neural networks with a step function as the transfer function. In that case of course the difference is that the logistic regression uses a logistic function and the perceptron uses a step function.

## Which of the following method gives the best fit for the logistic regression model?

Just as ordinary least square regression is the method used to estimate coefficients for the best fit line in linear regression, logistic regression uses maximum likelihood estimation (MLE) to obtain the model coefficients that relate predictors to the target.

The network problem does not immediately corrode. Ability to train machine: Artificial neural networks learn events and make decisions by commenting on similar events. Parallel processing ability: Artificial neural networks have numerical strength that can perform more than one job at the same time.

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## What are the advantages of neural networks over conventional computers?

What are the advantages of neural networks over conventional computers? Explanation: Neural networks learn by example. They are more fault tolerant because they are always able to respond and small changes in input do not normally cause a change in output.

## Why is neural network better than decision tree?

Neural networks are often compared to decision trees because both methods can model data that has nonlinear relationships between variables, and both can handle interactions between variables. … A neural network is more of a “black box” that delivers results without an explanation of how the results were derived.

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