prashanth
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• Number of hidden nodes: There is no magic formula for selecting the optimum number of hidden neurons. However, some thumb rules are available for calculating the number of hidden neurons. A rough ...

As per the literature, Schmidhuber, J. (2015). "Deep Learning in Neural Networks: An Overview". Neural Networks. 61: 85–117. arXiv:1404.7828free to read. doi:10.1016/j.neunet.2014.09.003. https://en....

As Johnnyboycurtis has answerd, non-parametric methods are those if it makes no assumption on the population distribution or sample size to generate a model. A k-NN model is an example of a non-...

The link provided in @itdxer's comment is great. Based on this link, I am writing this answer. Hyperparameter optimization is neural networks is a tedious job as it contains many set of parameters. ...

Yes, one must do normalization before using VarianceThreshold. This is necessary to bring all the features to same scale. Other wise the variance estimates can be misleading between higher value ...

t-SNE does not preserve distances, but it basically estimates probability distributions. In theory, the t-SNE algorithms maps the input to a map space of 2 or 3 dimensions. The input space is assumed ...

I am trying to answer my own question after doing few initial experiments. I tried the SMOTE technique to generate new synthetic samples. And the results are encouraging. It generates synthetic data ...

You could do min-max normalization (Normalize inputs/targets to fall in the range [−1,1]), or mean-standard deviation normalization (Normalize inputs/targets to have zero mean and unity variance/...

In Random forest, generally the feature importance is computed based on out-of-bag (OOB) error. To compute the feature importance, the random forest model is created and then the OOB error is ...

Matlab already provides a function for 'Random forest'. It is the 'TreeBagger' function. More info in https://in.mathworks.com/help/stats/treebagger.html To understand basics of Random forest ...

Yes, a predictive model can be developed to predict the Survived feature. A problem similar to this is given in the link https://www.kaggle.com/c/titanic. It has some very good tutorials too. This ...

I got the solution by adding a cropping layer in the end. The full code is as below: from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D from keras.models import Model # from ...

Feature selection is a more relevant process when there are large number of features (in the order of 1000's) with correlations betweent them and some being irrelevant as well. Here in this case, as ...

k-prototypes clustering might be better suited here. It combines k-modes and k-means and is able to cluster mixed numerical / categorical data. For R, use the Package 'clustMixType'. https://cran.r-...

To say that M1 significantly improved the result in compare with other methods, you must have to use statistical methods and tell that the measure or difference is statistically significant. To do ...

To check the performance of a split, as you mentioned MSE and RMSE are the popular approaches. RMSLE penalizes an under-predicted estimate greater than an over-predicted estimate ϵ is the RMSLE ...

For a deep neural network that you mention, finding an effective local minima is the key. As per the paper, Gülçehre, Çağlar, and Yoshua Bengio. "Knowledge matters: Importance of prior information ...

So my question is, why splitting my data at the beginning if I am assessing the performance of each of the 5 models with cross-validation. Every classifier has some parameter(s) to tune. You need to ...

Perhaps your model is over-fitting. Before you apply the classification algorithm, you need to divide the dataset to training and testing sets. The performance measures for the training and testing ...

Your dataset suffers from the imbalance problem. You can approach the problem possibly in two ways: Use sampling techniques to over-sample the minority class (the churn class), or undersample the ...

Transform predictors (input data) to a high-dimensional feature space. It is sufficient to just specify the kernel for this step and the data is never explicitly transformed to the feature space. This ...

Random forests for Regression is a suitable algorithm when there are mixed features (both numeric and categorical). I am assuming you are using Python for coding. Python has sklearn library which ...

The feature importance in Random Forest is based on out-of-bag error, and its value is>=0. As per the Wikipedia page, "To measure the importance of the j-th feature after training, the values ...

In machine learning, there is no one algorithm that’s always better than others which is as per the “No free lunch theorem”. Therefore, one has to try with different set of classifiers and choose the ...

You may use the code as below to plot the elbow curve. The input to the code below is the . data <- <input the data here> # Elbow Method for finding the optimal number of clusters set.seed(...

It is generally recommended to use more than one method for feature selection. For instance, taking a combination of outputs from Recursive feature elimination and Random forests can be effective to ...

It is alright to use different classifiers for feature selection and prediction. But generally it is recommended to see the features selected from multiple methods before finalizing the features. A ...

Feature selection is a recommended approach before predictive modeling because the presence of correlated or irrelevant features may make the built model bad. But domain knowledge has its own ...