Choosing the right model for the machine learning problems is very important. The right choice leads to better performance and more accurate results, and thus confidence in the predictions. Either way, we can hit and trial and use all possible models, but that will be a time-consuming and computationally intensive approach. So we had better make a decision which of the models is suitable for a given problem. There are some criteria and conditions that can be taken into account that will allow us to select the models. In this article, we will discuss the factors to consider when choosing a supervised learning model. The key points to be discussed in the article are listed below.

**contents**

- The supervised learning
- Factors to consider in supervised learning models
- bias-variance trade-off
- functional complexity
- The dimensionality of the input space
- The noise of the target
- heterogeneous data
- Conflicting data
- Interactions and non-linearities in features

Let’s start by understanding the supervised learning model.

**About the supervised learning model**

In machine learning, supervised learning is a type of learning that monitors or labels the data we use. The supervised learning models are the models that work on the basis of outputting results using inputs in the form of data. In essence, we can say that the models that are able to map an input to an output, based on the knowledge they have gained from some examples, can be called supervised learning models. The output of a supervised learning model can also be viewed as the inference of a function generated using labeled training data.

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In labeled training data, each sample should consist of one input data point and one output data point. There are several supervised learning models and these models have their different algorithms and way of working. The choice of any model can be made based on the data and the required power.

The algorithms in these models can be referred to as supervised learning algorithms and must be able to work in a supervised learning environment. These algorithms are designed to analyze the training data and according to the analysis they produce a function capable of mapping the unseen examples.

If an algorithm can correctly determine the classes of invisible examples, we can call it an optimal algorithm. The generation of the prediction by the supervised learning algorithms is done in a reasonable way by generalizing the training data to unseen scenarios.

There are different types of supervised learning algorithms and they can be used in different types of supervised learning programs. In generalization, we mainly work with two types of problems:

- regression analysis
- classification analysis

Some of the models for regression analysis are as follows:

- Linear regression
- Multilinear Regression
- time series modelling
- Neural Networks

Some of the models for classification analysis are as follows:

- Random Forest
- decision trees
- Naïve bias
- Neural Networks
- logistic regression

However, in the most recent scenario we can observe that we use classification models in regression analysis or vice versa, but this also has to make some of the changes in the algorithm of these models.

These algorithms are best in their place when used correctly and in this article our main focus is how to choose models for our projects, or we can say that we will discuss the points that make a model for ours select work . Let’s move on to the next section.

**Choice of supervised learning models**

In the above section we see the example of supervised learning models. The above names are very few, which means different options can be used to do supervised learning. Since no one model is best for all problems, how do we choose an optimal model for our problems? When choosing a model, various criteria and conditions must be taken into account. Some of them are as follows:

This is our first concept, which mainly tells about the flexibility of the model. As we fit the data, a model attempts to learn data by mapping the data points. Geometrically we can say that the model fits an area or line that covers all data points as indicated in the image below

In the image above, the red line represents the model and the blue dots are the data points. This is a simple linear regression model and things get critical when a model is biased on an input value instead of being biased on every data point or class. In this situation, the output provided by the model will be inaccurate.

Similarly, if the model has high variance for an input value, that means it will give a different output for a single input while being applied multiple times. This is also an inaccurate way of modeling. The bias situation occurs when the model is not flexible and the variance situation occurs when the model is very flexible.

The model chosen must be somewhere between highly flexible and non-flexible. The error in the prediction of the classifiers is partly related to the sum of the bias and variance of the model. The model we fit to the data should be able to fit the bias versus variance tradeoff.

Techniques such as dimensionality reduction and feature selection can help reduce the variance of the model, and some of the models carry parameters that can be adjusted to maintain the bias-variance tradeoff.

The amount of training data is closely related to the performance of each model. Because a model carries features underneath it, and when those features are simple, a model with little flexibility is better able to learn from the small amount of data.

However, the features of the model are complex, requiring a large amount of data for high performance and accuracy. In a state where the functions are highly complex, the models must be flexible with low distortion and high variance.

Models such as Random Forest and Support Vector Machines are highly complex models and can be selected with high dimensional data, and models with low complexity features are linear and logistic regression and can be used with small amounts of data.

Since the calculation below is always an estimated way of modeling, we should not use models with complex functions in a scenario where the amount of data is small.

**The dimensionality of the input space**

Above we discussed the function of the model. The performance of the model also depends on the dimensionality of the input data. If the features of the data are very sparse, learning the model can be poorly performed, even if the model’s functions rely on a smaller number of input features.

It is very easy to understand that the high dimension of the input can confuse the supervised learning model. So, in such a scenario where the dimensions of the input features are high, we need to choose the models that are flexible for their tuning so that the procedure has low variance and high bias.

But techniques such as feature engineering are also helpful here, since these methods are able to identify the relevant features from the input data. Also, domain knowledge can help to extract relevant data from the input data before applying it to the model.

**The noise of the target**

Above we saw how the dimensionality of the input affects the performance of the models. Sometimes the performance of the model can also be affected by the noise of the output variables of the target variables.

It’s very easy to understand, if the output variable is imprecise, then the model we apply tries to find a function that can be applied to give the required result and the model gets confused again. We always need to fit models in such a way that the model doesn’t try to find a function that exactly matches the training examples.

Applying the model to the data very carefully always leads to an overfitting of the model. In addition, an overfitting problem arises when the function that the model finds to apply to the data is very complex.

In these situations we need to have the data that has the target variable that can be easily modeled. If this is not possible, we need to adjust the model, which has higher bias and lower variance.

However, there are techniques such as stopping early that can prevent overfitting and techniques that can detect and remove the noise of the target variable. One of our articles provides information that can be used to prevent overfitting.

In the sections above, we discussed the dimensionality and noise of the input and target variables. In some scenarios we may find that we have data that has characteristics of different types, e.g. B. discrete, discrete ordered, count and continuous values.

With such data, we need to apply models that can apply a distance function underneath. Support vector machines with Gaussian kernels and k-nearest neighbors are the algorithms that are examples of such models and can be applied to heterogeneous data without generalizing the data.

Under a variety of conditions, we may find that the data we are asked to model have characteristics that are highly correlated with each other, and that simple supervised learning models perform very poorly on them. Under such conditions, we need to use models that can perform regularization. L1 regularization, L2 regularization, and dropout regularization are the models that can be used in such a situation.

**Interactions and non-linearities in features**

In a variety of data, we find that each input variable affects the position of the output individually. In such situations, linear function and distance function models may perform better. Models such as linear regression, logistic regression, support vector machines, and k-nearest neighbors have such features. And for complex interactions, neural networks and decision trees are the better option because they can find the interaction.

**last words **

In this article, we have discussed various criteria and conditions to consider when choosing a supervised learning model. Since there are different situations of modeling, the selection of models is a very complex task. We should know where to use which model.