What factors to consider when choosing a supervised learning model?
Choosing the right model for the machine learning problems is very important. The right selection leads to better performance and accurate results and hence trust in the predictions. Either we can go with hit and trial and employ all the possible models but that will be a time consuming and computationally expensive approach. So better we should make a decision which of the models will be suitable for a given problem. There are some criteria and conditions that can be considered based on which we can select the models. In this article, we are going to discuss the factors to consider when choosing a supervised learning model. The major points to be discussed in the article are listed below. Table of contents The supervised learningFactors to consider with supervised learning modelsBias-variance tradeoff   Function complexityThe dimensionality of the input spaceThe noise of the target Heterogeneous dataRebudenceous data   interactions and non-linearities in features Let’s start with understanding the 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 labelled. The supervised learning models are the models that work based on giving output using input in the form of data.  In the core, we can say that the models that are capable of mapping an input to an output based on the knowledge that they have gained using some examples can be called supervised learning models. The output a supervised learning model gives can also be considered as the inference of a function that is generated using labelled training data. Are you looking for a complete repository of Python libraries used in data science, check out here. In labelled training data, every sample should consist of an input data point and an output data point. There are several supervised learning models and these models have their different algorithms and nature of work. The selection of any model can be done based on the data and required performance. The algorithms inside these models 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 an algorithm can correctly determine the classes of unseen examples then we can call it an optimal algorithm. Generation of prediction by the supervised learning algorithms is done by generalizing the training data to unseen scenarios in reasonable ways. There are various kinds of supervised learning algorithms and they can be used in various kinds 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 Multi-linear regression Time series modellingNeural networks Some of the models for classification analysis are as follows: Random forest Decision treesNaive bias Neural networks Logistic regression However, in the recent scenario, we can be witnessed using classification models in regression analysis or vice versa but this also needs to perform some of the changes in the algorithm of these models. These all algorithms are best in their places if used properly and in this article, our main focus is on how we can select models for our projects or we can say we are going to discuss the points that make a model to be selected for our work. Let’s move toward the next section. Selection of supervised learning models In the above section, we can see the example of supervised learning models. The above-given names are very few, which means various 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 we can choose one optimal model for our problems. Some various criteria and conditions need to be considered while choosing a model. Some of them are as follows: Bias-variance tradeoff This is our first concept that mainly tells about the flexibility of the model. While we fit the data, one model tries to learn data by mapping the data points. Geometrically we can say the model fits an area or line that covers all of the data points as given in the following picture 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 things become critical when a model becomes biased to a value of input instead of being biased toward every data point or class. In this situation, the output given by the model will be inaccurate. Similarly, if the model becomes high variance for a value of input which means it will give different output for single input while applying it various times. This is also an inaccurate way of modelling. The bias situation happens when the model is not flexible and the variance situation happens when the model is very flexible. The chosen model needs to be in between the highly flexible and not flexible. The error in the prediction of the classifiers is some were related to the sum of bias and variance of the model. The model we are fitting on the data should be able to adjust the tradeoff between bias and variance. Techniques like dimensionality reduction and feature selection can help decrease the variance of the model and some of the models carry parameters with them that can be adjusted to maintain the tradeoff between bias and variance. Function complexity The amount of the training data is closely related to the performance of any model. Since a model carries functions under them and if these functions are simple then a model with low flexibility can learn better from the 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 highly 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 complex functions are linear and logistic regression and can be used with low amounts of data. Since the lower calculation is always an appreciated way of modelling we should not apply models with complex functions in a scenario where the amount of data is low. The dimensionality of the input space In the above, we have 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 the learning of the model can be low performing even when the functions of the model rely on a less number of input features. It is very simple to understand that the high dimension of the input can confuse the supervised learning model. So in such a scenario where the dimensions of input features are high, we need to select those models that are flexible for their tuning so that in the procedure there will be low variance and high bias. However, techniques such as feature engineering are also helpful here because these methods have the capability of identifying the relevant features from the input data. Also, domain knowledge can help extract relevant data from the input data before applying it to the model. The noise of the target In the above, we have seen how the dimensionality of the input affects the performance of the models. Sometimes performance of the model can also be affected by the noise of the output variable of the target variable. It is very simple to understand if there is inaccuracy in the output variable then the model we are applying will try to find a function that can be applied to provide the required outcome and again the model will be confused. We are always required to fit models in such a way that the model won’t attempt to find a function that exactly matches the training examples. Being very careful while applying the model to the data always leads to the overfitting of the model. Also, there will be an overfitting problem when the function the model is finding to apply to the data is very complex. In these situations, we are required to have the data that has the target variable that can be easily modelled. 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 possesses information that can be utilized to prevent overfitting. Heterogeneous data 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 have features of different types such as discrete, discrete ordered, counts, and continuous values. With such data, we are required to apply models that can employ a distance function under it. Support vector machines with Gaussian kernels and k-nearest neighbours are the algorithms that are examples of such models and can be applied to heterogeneous data without generalizing the data. Rebudenceous data In a variety of conditions, we may see that the data we are to model has features that are highly correlated to each other, and simple supervised learning models perform very 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. Interactions and non-linearities in features In a variety of the data, we find that each input variable impacts the position of the output individually. In such situations, models with linear function and distance functions can perform better. Models such as linear regression, logistic regression, support vector machines, and k-nearest neighbours have such functions. And in the case of 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 have discussed various criteria and conditions to consider when choosing a supervised learning model. Since there are different situations of modelling the selection of models is a very complex task we should know where to use which model.
Thera are many important factors that need to be considered while choosing a machine learning model.
A detailed coverage on many important factors that are need to be considered while choosing a supervised learning model
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["data science", "Go", "machine learning", "TPU", "AI", "neural network", "feature engineering", "Python", "Rust", "R"]
["AI", "machine learning", "neural network", "data science", "TPU", "Python", "R", "Go", "Rust", "feature engineering"]
Yugesh is a graduate in automobile engineering and worked as a data analyst intern. He completed several Data Science projects. He has a strong interest in Deep Learning and writing blogs on data science and machine learning.