Introduction of Hypothesis in Statistics and Machine Learning
What is Hypothesis in Statistics and Machine learning?
The topic of Hypothesis in Machine Learning can be confusing for beginner because it is related to Statistics(Statistical Hypothesis).
Here,we will study the difference between a hypothesis in science, in statistics, and in machine learning.
Table of content:-
- What is Hypothesis?
- Hypothesis in Statistics.
- Hypothesis in Machine Learning.
1.What is Hypothesis?
A hypothesis (plural hypotheses) is a proposed explanation for a phenomenon.
The hypothesis must be framed before the outcome of the test is known.
A good Hypothesis fits the evidence and can be used to make predictions about new observations.
The Hypothesis that best fits the evidence and can be used to make predictions is called a theory.
Scientific Hypothesis:-
People refer to a trial solution to a problem as a hypothesis, often called an “educated guess” because it provides a suggested outcome based on the evidence. However, some scientists reject the term “educated guess” as incorrect. Experimenters may test and reject several hypotheses before solving the problem. ~ wikipedia
2.Hypothesis in Statistics.
A Hypothesis is an assertion or conjecture about the parameter(s) of population distribution(s).
Much of statistics is concerned with the relationship between observations.
Statistical hypothesis tests are techniques used to calculate a critical value and it can be interpreted in order to determine how likely it is to observe the effect if a relationship does not exist.
If the likelihood is very small, then it suggests that the effect is probably real. If the likelihood is large, then we may have observed a statistical fluctuation, and the effect is probably not real.
Types of Hypothesis
Null Hypothesis(H0):- A Hypothesis which is to be actually tested for acceptence or rejection is termed as Null hypothesis.
Alternative Hypothesis(H1):- It is a statement about the population parameter, which gives an alternarive to the Null Hypothesis(H0), within the range of pertinent values of the parameter, i.e., if H0 is accepted, what hypothesis is to be rejected and vice versa.
In short, it is a probabilistic explanation about the presence of a relationship between observations.
3. Hypothesis in Machine Learning
A model that approximates the target function and performs mappings of inputs to outputs is called a hypothesis in machine learning.
The choice of algorithm (e.g. neural network) and the configuration of the algorithm (e.g. network topology and hyperparameters) define the space of possible hypothesis that the model may represent.
The framing of machine learning is common and help us to understand the choice of algorithm, the problem of learning and generalization, and even the bias-variance trade-off. For example, the training dataset is used to learn a hypothesis and the test dataset is used to evaluate it.
- h (hypothesis): A single hypothesis, e.g. an instance or specific candidate model that maps inputs to outputs and can be evaluated and used to make predictions.
- H (hypothesis set): A space of possible hypotheses for mapping inputs to outputs that can be searched, often constrained by the choice of the framing of the problem, the choice of model and the choice of model configuration.
In short, model that approximates a target function for mapping examples of inputs to outputs.
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