Gaussian | Cumulative |Uniform | Pareto | Bernoulli | Binomial Distributions Machine Learning
1. Gaussian Distribution:
Characteristics: Bell-shaped curve, symmetric around the mean.
Parameters: Mean (μ) and standard deviation (σ).
Use in ML: Commonly used due to the Central Limit Theorem. Many natural phenomena exhibit a Gaussian distribution.
2. Cumulative Distribution:
Characteristics: Represents the cumulative probability of a random variable being less than or equal to a specific value.
Use in ML: Useful for understanding the overall distribution of a dataset and calculating probabilities.
3. Uniform Distribution:
Characteristics: Constant probability across the entire range.
Parameters: Minimum and maximum values of the range.
Use in ML: Modeling situations where all outcomes are equally likely, often in random sampling.
4. Pareto Distribution:
Characteristics: Long-tailed distribution with a small number of high-magnitude events and a large number of low-magnitude events.
Parameters: Shape parameter (α) determines the shape of the tail.
Use in ML: Applicable in situations where a small number of factors contribute to the majority of the outcomes, such as wealth distribution or internet traffic.
5. Bernoulli Distribution:
Characteristics: Discrete distribution with two possible outcomes (0 or 1).
Parameter: Probability of success (p).
Use in ML: Fundamental for binary classification problems, representing the probability of success or failure for a single trial.
6. Binomial Distribution:
Characteristics: Discrete distribution representing the number of successes in a fixed number of independent trials.
Parameters: Number of trials (n) and probability of success (p).
Use in ML: Commonly used in scenarios involving a fixed number of repeated independent experiments, such as multiple trials in a binary classification problem.
These distributions have diverse applications in statistical modeling and machine learning, each providing a valuable framework for understanding different types of data and phenomena.
00:00 Gaussian Distribution
17:00 Cumulative Distribution
31:00 Uniform Distribution
47:00 Pareto Distribution
58:00 Bernoulli Distribution
1:09:00 Bionomial Distribution
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