Source: Deep Learning on Medium
What are the different models in Machine Learning?
What is a machine learning model?
A machine learning model can be a mathematical representation of a real-world process. The learning algorithm finds patterns in the training data such that the input parameters correspond to the target. The output of the training process is a machine learning model which you can then use to make predictions.
What is the difference between a model and an algorithm?
Algorithms are methods or procedures taken in other to get a task done or solve a problem, while Models are well-defined computations formed as a result of an algorithm that takes some value, or set of values, as input and produces some value, or set of values as output.
What are different models in Machine Learning?
- Decision Tree based methods
- Linear regression based methods
- Neural Network
- Bayesian Network
- Support Vector Machine
- Nearest Neighbor.
A decision tree or a classification tree is a tree in which each internal (non-leaf) node is labeled with an input feature.Decision tree learning is a method commonly used in data mining. The goal is to create a model that predicts the value of a target variable based on several input variables.
Linear regression is a linear approach to modeling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables). The case of one explanatory variable is called simple linear regression.
A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. In this sense, neural networks refer to systems of neurons, either organic or artificial in nature.
Bayesian networks are a type of probabilistic graphical model that uses Bayesian inference for probability computations. Bayesian networks aim to model conditional dependence, and therefore causation, by representing conditional dependence by edges in a directed graph
A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples
Nearest neighbor search (NNS)
Nearest neighbor search (NNS), as a form of proximity search, is the optimization problem of finding the point in a given set that is closest (or most similar) to a given point. Closeness is typically expressed in terms of a dissimilarity function: the less similar the objects, the larger the function values.
Model selection is the process of choosing between different machine learning approaches — e.g. SVM, logistic regression, etc — or choosing between different hyper parameters or sets of features for the same machine learning approach — e.g. deciding between the polynomial degrees/complexities for linear regression.