For every machine learning (ML) challenge, there are innumerable models and techniques to find the relevant solution. But while it is beneficial to have alternatives, picking an appropriate model is essential. Though there are several performance indicators for evaluation, not all solutions are designed for all problems. As such, it is pertinent to understand how to choose the right model for a problem.

In this post, we shall discuss the criteria that could help you decide on the best ML model specific to your business needs. However, to establish a profound grip on ML models, it’s essential to go through the basics of an ML algorithm first.

## What Is a Machine Learning Algorithm?

Machine Learning (ML) is an algorithm-based approach to evaluating data and finding patterns to make accurate estimates. Data is derived from multiple sources, including social media, company data, IoT sensors, etc. ML techniques help utilize data for automating procedures, making complicated predictions, personalizing experiences, etc.

Considering the vast range of ML algorithms, each type focuses on a specific task, factoring in data characteristics and project needs. Next, let’s discuss the different types of ML models.

## Different Types of Machine Learning Models

Machine Learning models can be classified into five broad categories:

**1. ****Classification Model**

The Classification Model predicts the class or type of object within a fixed range of options. The output, however, is a categorical variable. For instance, whether an email is spam or not can be categorized as a standard binary classification model.

Some of the commonly used classification models are the K-Nearest Neighbors’ algorithm, Naïve Bayes, Logistic Regression, SVM, Decision Tree, and Ensembles.

**2. ****Regression Model**

In machines, regression is a bunch of problems in which the output variable takes continuous values. For instance, predicting the latest fuel price is a standard regression.

There are different regression models – Linear Regression, Lasso Regression, Ridge Regression, SVM Regression, Decision Tree Regression, etc.

**3. ****Clustering Model**

Simply put, clustering is a broad term in which groups of similar objects are brought together. It assists in identifying similar objects without any manual intervention. To build valuable supervised machine learning models, we need homogeneous data. Clustering helps in achieving that more smartly and easily.

The commonly used clustering models are K means, K means++, K medoids, Agglomerative clustering, and DBSCAN.

**4. ****Dimensionality Reduction**

Dimensionality defines predictor variables used for predicting independent variables or targets. In real-world datasets, variables are higher in number. Excess variables lead to overfitting. Moreover, not all variables are useful for one goal. In most cases, it may be possible to preserve variances with fewer variables.

The common dimensionality models are PCA, TSNE, and SVD.

**5. ****Deep Learning**

The deep learning model comprises a subset of ML featuring neural networks. Depending on the neural network architecture, the most important deep learning models are multi-layer perceptron, Boltzmann machines convolution neural networks, recurrent neural networks, autoencoders, etc.

## How to Choose the Right Machine Learning Model?

Noted below are some of the key considerations for choosing the right ML algorithm model:

**Identify The Problem**

Each ML model is designed to handle specific challenges. Thus, you must understand the problem and the best algorithm to resolve it. Without getting into too many details, you can choose from the three major types of ML algorithms – Supervised, Unsupervised, or Reinforcement Learning.

**Varied Execution Time**

Models do not necessarily have a fixed execution time. Training time is proportional to the amount of dataset and target accuracy. In addition, consider how the time for training affects the project. If the project is within the application, but you lack training resources, then consider a model which doesn’t require resources.

However, if you are involved in a research project and wish to push your limit, you may need a longer time to train.

**Training Set Size**

The size of the training set plays a crucial role in selecting the model. There are two classifiers: high bias/low variation and low bias/high variation. The former has a slight advantage over the latter.

However, as the sets expand, low bias/high variation classifiers start winning as high bias classifiers fail to build correct models. So, always consider the importance of overfitting. Some algorithms overfit quickly, which compels users to think about the quantity of training data and how much is enough.

**Precision**

The required accuracy varies depending on the applications. Sometimes, a “sufficient” estimate may reduce processing time significantly. Furthermore, the approximation approach is specifically unaffected by overfitting. Thus, it is crucial to set the accuracy threshold.

For instance, if a project requires 90% accuracy, you may not consider doing hyperparameter tuning after reaching the threshold. For starters, you can save a lot on computer resources for training models further and improving accuracy. In addition, you can reduce the complexity of models. In business settings, it is always easier to explain linear regression instead of Multi-layer Perceptron to clients.

**Features**

Datasets may have a large number of features compared to data points. Due to a large number of characteristics, few ML algorithms are logged, thus making the training time unfeasible. For instance, too many features affect SVM performance. Consequently, if the dataset contains too many features, you may prefer using Neural Network.

The other way to deal with innumerable features is to drop unwanted ones. Use dimensionality reduction algorithm to reduce features.

**Parameters**

Parameters influence the algorithm’s behavior, like repetitions and error tolerance. Algorithms with higher parameters require the best trial and error to identify successful combinations. Simply put, the more parameters model features, the more time it takes to improve the model for hyperparameter tuning.

Although several parameters give greater flexibility, it is extremely sensitive to achieve n correct values of algorithm accuracy and training time. Consider this carefully before choosing the ML model.

**Linear/Non-Linear**

Linearity is a common part of ML models, including support vector machines, regression analysis, and linear regression. While these expectations may not be directly harmful to certain cases, they may reduce others’ efficiency. Despite all the perils, linear algorithms are commonly used for defense. Algorithmically, they are easier and quicker to train.

## Final Word

When you choose an ML model, work iteratively. Test input data and analyze the model’s performance to make the best choice. Furthermore, to deliver the best solution to a problem, know the rules, business requirements, and concerns of stakeholders.