Hypothesis is used in research and analytics to understand the relationship between dependent and independent variables. Hypothesis building can begin in the data exploration stage, but it becomes more mature and perfect in the conclusion or prediction phase.
Key considerations of Hypothesis Building
- Hypothesis are testable explanations of a problem or observation.
- Formulating a hypothesis is used for both quantitative and qualitative analysis to address research problems.
- Hypothesis that suggests a casual relationship Involves at least one independent variable which is presumed to affect the other.
- An independent variable is one whose value is manipulated by the researcher or data scientist.
- A dependent variable is a variable whose values are presumed to change as a result of changes in the independent variable.
Hypothesis Building using Feature Engineering.
Hypothesis building, a way to design models and predict the unknown, can be done using feature engineering. This includes Identifying meaningful features based on data domain knowledge.
Feature engineering involves domain expertise to:
- Make sense of data.
- Construct new features from raw data automatically.
- Construct new features from raw data manually.
Hypothesis Building using a model
There are three phases to hypothesis building which are model building, model evaluation, and model deployment.
Phase 1: – Model Building
- Identifying the best input variables for the model.
- Evaluate the model’s capacity to forecast with these variables.
Phase 2: – Model Evaluation
- Train and test the model for accuracy.
- Optimize model accuracy, performance, and comparisons with other models.
Phase 3: – Model Deployment
- Use the model for prediction.
- Use the model to compare actual outcomes with expectations.
Population is a large dataset and samples are a part of it. A sample drawn from a population should have all the main attributes or features which represent the characteristics of the population. An ideal sample can be treated as the population itself and the hypothesis outcome for a sample would hold true for the entire population.
The process of calculating the difference between the two means is “hypothesis testing”.
Hypothesis Testing Process
Two kinds of hypothesis can be made initially. Choosing the training and test dataset and evaluating them with the null and alternative hypothesis.
- Alternative hypothesis: – This hypothesis indicates that the proposed model outcome is accurate and matches the data. There is a difference between the means of S1 and S2.
- Null hypothesis: – Null Hypothesis opposite of the alternative hypothesis. There is no difference between the means of S1 and S2.
The process of hypothesis testing begins by dividing a big dataset into training and test datasets, irrespective of the size of the dataset. This is one of the best techniques to design an accurate model. The training dataset is used to build a new proposed model. It makes use of the available features and responses of the data sample. The test data set acts as new unseen data.
The Null Hypothesis proposed model does not predict better than the existing model.
The Alternative hypothesis will be proven right if the proposed model predicts better than the existing model.
Note: – Usually the training dataset is between 60% and 80% of the bug dataset and the test dataset is between 20% and 40% of the big dataset.