What Factors to Consider When choosing a supervised learning model?

Choosing the right model is very important for machine learning problems. The right selection leads to better performance and accurate results and hence trust in the predictions. Either we can go and hit and trial and employ all the possible models but that is a time consuming and computationally expensive approach. So better we should make a decision on which models to use for a given problem. There are some criteria and conditions that can be considered based on which models we can choose. In this article, we are going to discuss the factors to consider when choosing a supervised learning model. The major points to be discussed are listed below.

Table of contents

  1. The supervised learning
  2. Supervised learning models with factors to consider
    1. Bias-variance tradeoff
    2. Function complexity
    3. The dimensionality of the input space
    4. The target of noise
    5. Heterogeneous data
    6. Rebudenceous data
    7. features in interactions and non-linearities

Let’s start with a supervised learning model.

About the supervised learning model

In machine learning, supervised learning is a type of learning where the data we use is supervised or labeled. The supervised learning models are the models that work in the form of data-driven input. In the core, we can say that models that are capable of mapping to an input based on knowledge that they have acquired are some examples of what they are using as supervised learning models. The output is a supervised learning model that can also be considered as a function of the reference that is generated using labeled training data.

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In labeled training data, each sample should consist of an input data point and an output data point. There are many supervised learning models and these models have their different algorithms and nature of work. The selection of any model can be based on data and required performance.

The models inside these algorithms can be called supervised learning algorithms and they must be capable of working in a supervised learning environment. These algorithms are designed to analyze the training data and according to the analysis they produce a function that is capable of mapping the unseen examples.

If we can call it an optimal algorithm then we can call it an optimal algorithm. Generated prediction by supervised learning algorithms is done by generalizing the training data to unseen scenarios in reasonable ways.

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

  • Regression analysis
  • Classification analysis

Regression analysis for some of the models is as follows:

  • Linear regression
  • Multi-linear regression
  • Time series modeling
  • Neural networks

The classification analysis for some of the models is as follows:

  • Random Forest
  • Decision trees
  • Naive bias
  • Neural networks
  • Logistic regression

However, in the recent scenario, we can use the classification models used in the regression analysis or vice versa to perform some of the changes in the algorithm.

These are all algorithms that are best used in their places and in this article, our main focus is on how we can choose models for our projects or what we are going to do with the points we make for a model. . Let’s move on to the next section.

Supervised learning models of selection

In the above section, we can see examples of supervised learning models. The above-given names are very few, which means that many options can be utilized to perform supervised learning. Since no model works best for all the problems, one thing that comes to mind is how can we choose the optimal model for our problems. A model of when to consider some different criteria and conditions. Some of them are as follows:

This is our first concept that basically says about flexibility. While we fit the data, one model tries to learn data by mapping the data points. Geometrically we can say that the model fits an area or line that covers all of the data points given in the following figure

In the above image, the red line represents the model and the blue dots are the data points. This is a simple linear regression model and when everything becomes critical a model becomes biased to a value that is biased toward each data point or class. In this situation, the output given by the model will be inaccurate.

Similarly, if the model becomes a value of input for a high variance, it will give you different outputs while applying it to multiple inputs. This is also an inaccurate way of modeling. The model is not very flexible when the bias situation occurs and the model is very flexible.

The chosen model needs to be highly flexible and not flexible. The classifiers of error are some of the related to the sum of bias and variance of the model. The model we fit on the data should be able to adjust the tradeoff between bias and variance.

Techniques such as dimensionality reduction and feature selection can help reduce the variance of the model and some of the models with the parameters of the tradeoff between bias and variance.

The amount of the training data is more closely related to any model of performance. Since a model carries functions under them and these functions are simple then a model with low flexibility can learn from a small amount of data.

But the functions of the model are complex, so they need a high amount of data for high performance and accuracy. In a condition where the functions are the most complex the models need to be flexible with low bias 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 functions are linear and logistic regression and can be used with low-cost data.

Since the lower calculation is always an appreciated way of modeling we should not apply models with complex functions where the amount of data is low.

  • The dimensionality of the input space

In the above, we have discussed the model of function. The model of performance also depends on the dimensionality of the input data. If the features of the data are very sparse, the model of learning can be low even when the functions of the model rely on a lower number of input features.

It is very simple to understand that a high-dimensional input can be confused with a supervised learning model. So in such a scenario where the input features of the dimensions are high, we need to select the models that are flexible for their tuning so that there is a low variance and high bias.

However, techniques such as feature engineering are also useful here because of the capability of identifying the relevant features from the input data. Also, the domain knowledge can help extract the relevant data from the input data before applying it to the model.

  • The target of noise

In the above, we have seen how the dimensionality of the input affects the performance of the models. Sometimes the model of performance can also be affected by the noise of the output variable.

It is very simple to understand if there is inaccuracy in the model then the model we are trying to apply is a function that can be applied to the desired result and again the model will be confused. We are always required to fit the models in such a way that the model will try and find a function that matches the training examples.

The model of overfitting the data always leads to the application of being very careful. Also, when there is an overfitting problem, the function of the model is to find and apply the data to the very complex.

In these cases, we are required to have the data that the target variable has that can be easily modeled. If it is not possible we are required to fit the model that has higher bias and lower variance.

However, there are techniques like early stopping that can prevent overfitting and techniques that can detect and remove the noise of the target variable. One of our articles owns information that can be utilized to prevent overfitting.

In the above sections, we have discussed the dimensionality and noise of the input and the target variable. In some scenarios, we can find that we have data that has different types of features such as discrete, discrete ordered, counts, and continuous values.

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

In a variety of conditions, we may see that the data we have for the model are features that are highly correlated to each other, and that simple supervised learning models perform poorly with them. In such conditions, we are required to use models that can perform regularization. L1 regularization, L2 regularization, and dropout regularization are the models that can be utilized in such a situation.

  • Features in Interactions and Non-Linearities

In a variety of data, we find that each input variable impacts the position of the output individually. In such cases, models with linear function and distance functions can perform better. Models have such functions as linear regression, logistic regression, support vector machines, and k-nearest neighbors. And in the case, the complex interaction neural networks and decision trees are the better option, because of their capability of finding the interaction.

Final words

In this article, we discuss the different criteria and conditions for choosing a supervised learning model. Since there are a number of different modeling the selection of models is a very complex task we need to know where to use.

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