Differences between Statistical modeling and Machine learning modeling

Statistics:

Statistics is the branch of mathematics dealing with the collection, analysis, interpretation, presentation, and organization of numerical data.

Statistics are mainly classified into two subbranches:

• Descriptive statistics: These are used to summarize data, such as the mean, standard deviation for continuous data types (such as height, blood pressure), whereas frequency and percentage are useful for categorical data (such as gender).
• Inferential statistics: Many times, a collection of the entire data is impossible, hence a subset of the data points is collected, also called a sample, and conclusions about the entire population will be drawn, which is known as inferential statistics. Inferences are drawn using hypothesis testing, the estimation of numerical characteristics, the correlation of relationships within data, and so on.

Machine learning

Machine learning is the branch of computer science that utilizes past experience to learn from and use its knowledge to make future decisions. Machine learning is at the intersection of computer science, engineering, and statistics. The goal of machine learning is to generalize a detectable pattern or to create an unknown rule from given examples.

Machine learning is broadly classified into three categories

1. Supervised learning: Classification problems and Regression problems are two categories
2. Unsupervised learning: Dimensionality reduction and clustering are two categories
3. Reinforcement learning: This allows the machine or agent to learn its behavior based on feedback from the environment In reinforcement learning, the agent takes a series of decisive actions without supervision and, in the end, a reward will be given, either +1 or -1. Based on the final payoff/reward, the agent reevaluates its paths. Reinforcement learning problems are closer to artificial intelligence methodology rather than frequently used machine learning algorithms.

Differences between statistical modeling and machine learning

up next will meet you with “Statistical Knowledge required for DataScience”

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