Machine Learning Model
Table of Contents
What precisely is a Machine Learning Model?
A machine learning model is a data file trained to recognize specific patterns. You prepare a model on a set of data by providing it with an algorithm that it can use to reason about and learn from that data.
After you’ve trained the model, you can use it to reason over data it’s never seen before and make predictions about it. For example, assume you want to create an application that can recognize a user’s emotions based on their facial expressions. You can train a model by feeding it images of faces labeled with different emotions and then use that model in an application that can detect any user’s emotion
When Should Machine Learning Be Used?
Good machine learning scenarios frequently share the following characteristics: They involve a repeated decision or evaluation that you want to automate and require consistent outcomes.
It is difficult or impossible to describe a decision’s solution or criteria explicitly. You have labeled data or examples where you can describe the situation and map it to the desired outcome.
Machine Learning Models: An Overview
A machine learning model is the mathematical representation of a real-world process and results from the training process. Machine learning algorithms discover patterns in the training dataset, which is used to approximate the target function and is in charge of mapping inputs to outputs from the available dataset. Depending on the task, these machine learning methods remain classified as Classification models, Regression models, Clustering, Dimensionality Reductions, Principal Component Analysis, and so on.
Types of Machine Learning Models
Created on the type of tasks, we can classify machine learning models into the following types:
- Classification models
- Regression models
- Dimension reduction
- Deep learning etc.
Concerning machine learning, classification is the task of predicting the type or class of an object within a finite number of possibilities. The output variable for classification is always categorical. For example, a standard binary classification task is to predict whether an email is spam or not. Let us now note some important models for classification problems.
- K-Nearest Neighbor Algorithm – Simple but computationally intensive.
- Naïve Bayes Creäte on Bayes theorem.
- Logistic Regression – Linear Ideal for Binary Classification.
- SVM – can be used for binary/multi-class organizations.
- Decision Tree – “If Else” based classifier, extra robust to outliers.
- Ensembles – A combination of multiple machine learning models that have remained combined to produce better results.
In a machine, regression learning is a set of problems where the output variable can take on continuous values. For example, predicting the price of an airline can be considered a standard regression task. But, first, let us note some important regression models used in practice.
- Linear Regression – The simplest basic model for a regression task works well only when the data are linearly separable and very little or no multicollinearity remains found.
- Lasso Regression – Linear regression with L2 regular.
- Ridge Regression – Linear regression with L1 normal.
- SVM regression
- Decision Tree Regression etc.
Simply put, clustering is the task of similar grouping objects together. It helps to identify similar things without manual intervention automatically. Without homogeneous data, we cannot build effective supervised machine learning models (models that need to remain trained using manually edited or labeled data). Clustering helps us do this smarter. Below are some of the widely used clustering models:
- K stands for – simple but suffers from high variability.
- K stands for++ – Modified version of K stands for.
- To medoids.
- Agglomerative clustering – Hierarchical clustering model.
- DBSCAN – Density-based clustering algorithm etc.
4) Reduction of Dimensions
Dimensionality is the number of predictor variables used to predict the independent variable or target. In real data sets, the number of variables is often too high. Unfortunately, too many variables also introduce the curse of overfitting into models.
In practice, among this large number of variables, not all variables contribute equally to the target, and in many cases, we can preserve variances with fewer variables. Let’s list some commonly used models for dimension reduction.
PCA – Creates a smaller number of new variables from many predictors. The new variables are independent of each other and then less interpretable.
TSNE – Provides lower dimensional embedding of higher dimensional data points.
SVD – Singular Value Decomposition decomposes a matrix into smaller parts for efficient computation.
5. Deep Learning
Deep learning is a subsection of machine learning that deals with neural networks. Based on the neural network architecture, let’s list the critical deep learning models:
- A multilayer perceptron
- Convolutional neural networks
- Recurrent neural networks
- Boltzmann machine
- Autoencoders etc.
Which model is the Best?
Above, we took ideas for a lot of machine learning models. Now the obvious question that comes to mind is, “Which one is the best model?” Of course, it depends on the problem and other related attributes such as outliers, the volume of data available, data quality, feature engineering, etc.
In practice, starting with the simplest model applicable to a given problem is always better and gradually increases complexity by fine-tuning the parameters and cross-validation. There is a saw in the data science world – ‘Cross-validation is more trustworthy than domain knowledge.
How to build a Model?
Let’s build a simple logistic regression model using the Scikit Learn python library. For simplicity, we assume that the problem is a standard classification model, “train.csv” is the train, and “test.csv” is the train and test data.
This article discussed important machine learning models for practical purposes and how to create a simple model in python. Choosing the suitable model for a specific use case is very important to get the correct result for a machine learning task.
To compare performance between different models, evaluation metrics or KPIs remain defined for specific business problems, and after applying statistical performance control, the best production model remains selected.
Also read: Foundations of Artificial Intelligence
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