It is always said that hyper-parameter Tuning is an iterative process and there is no shortcut to arrive at the best hyper-parameter in machine learning that your model requires. Having said that, there are certain factors which give you direction in which you should start tuning your hyper-parameters in machine learning and neural network algorithms.

## Machine Learning & Neural Network Algorithms

Generally, the accuracy metric that is monitored for ML and DL algorithms are **Root Mean Square Error (RMSE) or Mean Absolute Error (MAE)**. Hyper-parameter tuning is done to reduce the magnitude of these errors. So, how do we understand that the error is due to variance or bias? The image below describes on how you can infer whether your model has a high bias or a high variance -

Low variance (high bias) algorithms tend to be** less complex** with simple or rigid underlying structure.

On the other hand, low bias (high variance) algorithms tend to be **more complex** with a flexible underlying structure.

So, to summarise the Variance-Bias tradeoff, we are often in one of the following two situations when tuning our hyper-parameters i.e High Variance or High Bias.

#### How to overcome High Bias error?

#### How to overcome High Variance error?

#### Boosting and Bagging

Boosting is based on weak learners (high bias, low variance). In terms of decision trees, weak learners are shallow trees, sometimes even as small as decision stumps (trees with two leaves). Boosting reduces error, mainly by reducing bias (and also to some extent variance, by aggregating the output from many models).

On the other hand, Random Forest uses fully grown decision trees (low bias, high variance). It tackles the error reduction task in the opposite way: by reducing variance. The trees are made uncorrelated to maximise the decrease in variance, but the algorithm cannot reduce bias (which is slightly higher than the bias of an individual tree in the forest). Hence, the need for large, unpruned trees, so that the bias is initially as low as possible.

#### Machine Learning

##### Depth of the tree for tree based algorithms

One straight-forward way is to limit the maximum allowable tree depth. The common way for tree based algorithms to overfit is when they get too deep. Thus, you can use the maximum depth parameter as the regularisation parameter — making it smaller will reduce the overfitting and introduce bias, increasing it will do the opposite.

Larger the depth, more complex the model; higher chances of overfitting. There is no standard value for max_depth. Larger data sets require deep trees to learn the rules from data.

The depth of the tree should be tuned using cross validation.

##### Gamma

##### Lambda

##### Alpha

#### Cross Validation

Cross Validation is a technique which involves reserving a particular sample of a data set on which you do not train the model. Later, you test the model on this sample before finalizing the model.

One widely used cross validation technique is k- fold cross validation. Here are the quick **steps**:

**The average of your k recorded errors is called the cross-validation error**and will serve as your performance metric for the model.

Below is the visualization of how a k-fold validation works for k=10.

Always remember, **lower value of K is more biased and hence, undesirable. On the other hand, a higher value of K is less biased**, but can suffer from large variability. It is good to know that, smaller value of k always takes us towards validation set approach, where as higher value of k leads to LOOCV approach.

Hence, it **is often suggested to use k=10.**

#### Neural Networks

Both MLPRegressor and MLPClassifier use parameter alpha for regularization (L2 regularization) term which helps in avoiding overfitting by penalizing weights with large magnitudes.

**Alpha is a parameter for regularization** term, aka penalty term, that combats overfitting by constraining the size of the weights. **Increasing alpha may fix high variance **(a sign of overfitting) by encouraging smaller weights, resulting in a decision boundary plot that appears with lesser curvatures. Similarly, **decreasing alpha may fix high bias **(a sign of under-fitting) by encouraging larger weights, potentially resulting in a more complicated decision boundary.

When you are tuning a neural network, based on whether your initial model results indicate a high variance or a high bias, the alpha value can be increased or decreased accordingly.

Bias is the difference between your model’s expected predictions and the true values.

That might sound strange because shouldn’t you “expect” your predictions to be close to the true values? Well, it’s not always that easy because some algorithms are simply too rigid to learn complex signals from the dataset.

Imagine fitting a linear regression to a dataset that has a non-linear pattern:

No matter how many more observations you collect, a linear regression won’t be able to model the curves in that data! This is known as **under-fitting.**

Variance refers to your algorithm’s sensitivity to specific sets of training data.

High variance algorithms will produce drastically different models depending on the training set.

For example, imagine an algorithm that fits a completely unconstrained, super-flexible model to the same dataset from above:

As you can see, this unconstrained model has basically memorized the training set, including all of the noise. This is known as **over-fitting**.

There are some ways you can hypertune the complexity of the algorithm that you are working on:

#### K-Nearest Neighbors

Increasing “k” will decrease variance and increase bias.

Decreasing “k” will increase variance and decrease bias.

#### Regression

Increasing the degree of the polynomial would make it more complex.

Decreasing the degree of the polynomial would decrease the complexity of the model.

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