Statistics is a field of mathematics that is key behind to solving any Machine Learning Algorithm and to find good performance of our model. Machine learning is constructed upon an applied mathematics framework.
So, It should be quite obvious that machine learning works on data, and we can describe the data with the help of statistical framework.
This article covers the role of Statistics within the field of Machine Learning. Though I am presumptuous that you are aware of the types and basic concepts of Statistics.
Statistical learning plays a key role in several areas of science, finance and industry. Here square measure some samples of learning problems:
- Predict whether or not a patient, hospitalized due to a heart attack, will have a second heart attack or not. The predictions will be based on demographic data, diet charts and clinical measurements of that patient.
- Predict the value of a stock in 6 months from now, based on company performance measures and also economic data.
- Identify the numbers during a written postal code, from a digitized image.
- Estimate the quantity of aldohexose within the blood of a diabetic person, from the infrared spectrum of that person’s blood.
- Identify the danger factor for prostate cancer, based on clinical and demographic variables.
We also need statistics information virtually at every step of the Machine Learning model and without having a good knowledge of Role statistics you aren’t able to achieve the best performance for the model.
Role of Statistics in Downside Framing
Perhaps in a predictive modelling problem the biggest task is the framing of the problem.
At first it is the selection of the type of problem, e.g. regression or classification, and may be the structure and kinds of the inputs and outputs for the problem statement.
So, This step requires domain information regarding the type of your data. Applied statistical technique that can aid in the EDA throughout the framing of a haul include:
- EDA: Many times it’s very important to analyse our dataset to find the nature and flow of the data within the dataset. There is a pre defined method i.e., describe() function in Pandas DataFrame that returns the following basic statistical properties for each & every integer type attributes−
- Count
- Mean
- Standard Deviation
- Minimum Value in the attribute
- Maximum value in the attribute
- 25%
- Median i.e. 50%
- 75%
We can also use different visualization tools for a more robust understanding of the dataset.
- Data Mining: Automatic discovery of structured relationships and patterns within the dataset.
Statistics in Data Preparation
Statistical strategy square measures are required in the preparation of training and testing data for your machine learning model. Although, these are common or standard tasks that you simply could use or explore during a machine learning project.
These tasks includes-
- Data Cleaning: Try to identify and correct the mistakes or errors in our dataset.
- Feature Selection: Try to identify the input features that are most relevant to the task.
- Data Transforms: Try to change the scale or distribution of the features.
- Feature Engineering: Try to derive new features from the available data.
- Dimensionality Reduction: Making compact projections of the dataset.
Each of these above tasks have a whole field of study with specialised algorithms.
A basic understanding of data distributions, descriptive statistics, and data visualization is very important to help us to identify the methods to choose when performing these tasks.
Role of Statistics in Model Evaluation
Statistical strategies square measures needed while evaluating the skill of a machine learning model on data not seen throughout training.
Generally, the planning of this process is termed as experimental design. It is a whole subfield of statistical methods.
Model Evaluation includes-
- Hold-Out: During this technique, the mostly dataset are randomly divided into three subsets: Training set, Validation set, and Test set.
- Cross-Validation: When only a limited amount of data is available, to achieve an unbiased estimate of model performance, we use k-times cross-validation.
Role of Statistics in Model Selection
The goal of model selection is to decide the sparse model that adequately explains the data.
Statistical methods are required while selecting the final model configuration to use for a predictive modelling problem.
Methods of Model Selection includes-
- Forward Selection: Begin with nothing but an intercept in the model; test the addition of each feature using a chosen criterion; add the feature (if any) that gives a positive point to the model; repeat until none improves the model.
- Backward Selection: Start with all possible features in the model; test the deletion of each feature using a chosen criterion; remove the feature (if any) that can improve the model the most by being deleted; repeat until no further improvement is possible.
- Stepwise Selection: A combination of the above methods; test at each stop for features to be added OR removed.
This might include the use of hypothetical Testing of statistics.
Statistics in Model Presentation
Once a final model has been trained, it can be presented to stakeholders before being used or deployed to make actual predictions on real data.
This includes techniques for-
- Summarize the expected skill of the model on average.
- Quantify the expected variability of the model’s competence in practice
Methods from the field of estimation statistics can be used to quantify the uncertainty of the estimated competence of the machine learning model using tolerance intervals and confidence intervals
Role of Statistics in Prediction
Finally, it’ll return time to start using a final model to form predictions for the new data where we actually don’t know about the real outcome.
This includes techniques for-
- Quantifying the expected variability for the prediction.
This would possibly include estimation statistics like prediction intervals.
Written by: Sachin Yadav
Reviewed By: Vikas Bhardwaj
If you are Interested In Machine Learning You Can Check Machine Learning Internship Program
Also Check Other Technical And Non Technical Internship Programs
