Statistics for machine learning
Introduction
The word statistic conveys a variety of meaning to people in different walks of life.
The word statistic comes from Italian word Statista.
Sir Ronald Aylmer Fisher (1890-1962), renowned as "his time's greatest scientist," was a British
statistician and biologist who made significant contributions to experimental design and population
genetics. He is widely regarded as the "Father of Modern Statistics and Experimental Design."
Statistics is a branch of mathematics that deals with collecting, analyzing, interpreting, and
visualizing empirical data. Descriptive statistics and inferential statistics are the two major areas of
statistics. Descriptive statistics are for describing the properties of sample and population data (what
has happened). Inferential statistics use those properties to test hypotheses, reach conclusions, and
make predictions (what can you expect). Statistical methods help quantify uncertainty and variability
in data, allowing researchers and analysts to make data-driven decisions with confidence.
Machine Learning Statistics: In the field of machine learning (ML), statistics plays a pivotal role in
extracting meaningful insights from data to make informed decisions. Statistics provides the
foundation upon which various ML algorithms are built, enabling the analysis, interpretation, and
prediction of complex patterns within datasets
The uses of statistics in machine learning are incredible.
We have elaborated each one of them below.
1. Framing the problem
Problem framing is a prominent point under statistics in machine
2. Data understanding
Data understanding refers to grasping the relationship between variables and their distribution.
3. Data cleaning
It refers to the process of identifying and repairing issues related to the data.
4. Date selection and preparation
Not every variable or observation is relevant while modeling. The process of data selection is where
we reduce the data to make it relevant for predictions
5. Model evaluation
Evaluating the learning method is a crucial part of predictive modeling problems.
6. Configuration of the model
The learning methods included in the suite of hyper-parameters in a machine learning algorithm are
flexible to be tailored as per a given problem.
Introduction
The word statistic conveys a variety of meaning to people in different walks of life.
The word statistic comes from Italian word Statista.
Sir Ronald Aylmer Fisher (1890-1962), renowned as "his time's greatest scientist," was a British
statistician and biologist who made significant contributions to experimental design and population
genetics. He is widely regarded as the "Father of Modern Statistics and Experimental Design."
Statistics is a branch of mathematics that deals with collecting, analyzing, interpreting, and
visualizing empirical data. Descriptive statistics and inferential statistics are the two major areas of
statistics. Descriptive statistics are for describing the properties of sample and population data (what
has happened). Inferential statistics use those properties to test hypotheses, reach conclusions, and
make predictions (what can you expect). Statistical methods help quantify uncertainty and variability
in data, allowing researchers and analysts to make data-driven decisions with confidence.
Machine Learning Statistics: In the field of machine learning (ML), statistics plays a pivotal role in
extracting meaningful insights from data to make informed decisions. Statistics provides the
foundation upon which various ML algorithms are built, enabling the analysis, interpretation, and
prediction of complex patterns within datasets
The uses of statistics in machine learning are incredible.
We have elaborated each one of them below.
1. Framing the problem
Problem framing is a prominent point under statistics in machine
2. Data understanding
Data understanding refers to grasping the relationship between variables and their distribution.
3. Data cleaning
It refers to the process of identifying and repairing issues related to the data.
4. Date selection and preparation
Not every variable or observation is relevant while modeling. The process of data selection is where
we reduce the data to make it relevant for predictions
5. Model evaluation
Evaluating the learning method is a crucial part of predictive modeling problems.
6. Configuration of the model
The learning methods included in the suite of hyper-parameters in a machine learning algorithm are
flexible to be tailored as per a given problem.
