statistics for machine learning is the study of collecting, analyzing and interpreting data to help build better machine learning models. it provides the mathematical foundation to understand data patterns, make predictions and evaluate model performance.

it helps in understanding data distribution, variability and selecting the most useful features.
it is used to validate model results and make decisions under uncertainty using hypothesis tests, confidence intervals and bayesian methods.

how_statistics_is_used_in_machine_learning — machine learning statistics

why learn statistics for machine learning

understand the data before training models.
choose the right algorithms for specific problems.
evaluate model accuracy and performance.
handle uncertainty and variability in real-world data.

applications of statistics in machine learning

statistics is a key component of machine learning, with broad applicability in various fields.

feature engineering: selecting and transforming useful variables.
image processing: analyzing patterns, shapes and textures.
anomaly detection: spotting fraud or equipment failures.
environmental studies: modeling land cover, climate and pollution.
quality control: identifying defects in manufacturing.

types of statistics

there are commonly two types of statistics, which are discussed below:

descriptive statistics: "descriptive statistics" helps us simplify and organize big chunks of data. this makes large amounts of data easier to understand.
inferential statistics: "inferential statistics" is a little different. it uses smaller data to draw conclusions about a larger group. it helps us predict and draw conclusions about a population.

descriptive statistics

descriptive statistics summarize and describe the features of a dataset, providing a foundation for further statistical analysis.

mean	median	mode
mean is the sum of all values divided by the total number of values. mean μ = sum of values \ number of values	median is the middle value of sorted data for odd number of data points: median = (\frac{n+1}{2})^{th} for even number of data points: median = average of (\frac{n}{2})^{th} value and its next value	mode is the most frequently occurring value in the dataset.

mean

median

mode

mean is the sum of all values divided by the total number of values.
mean μ = sum of values \ number of values

median is the middle value of sorted data

for odd number of data points:

median = (\frac{n+1}{2})^{th}

for even number of data points:

median = average of (\frac{n}{2})^{th} value and its next value

mode is the most frequently occurring value in the dataset.

measures of dispersion

range: the difference between the maximum and minimum values.
variance: the average squared deviation from the mean, representing data spread.
standard deviation: the square root of variance, indicating data spread relative to the mean.
interquartile range: the range between the first and third quartiles, measuring data spread around the median.

measures of shape

skewness: indicates data asymmetry.

kurtosis: measures the peakedness of the data distribution.

types-of-skewed-data — types of skewed data

inferential statistics

inferential statistics involve making predictions or inferences about a population based on a sample of data.

population and sample

population: the entire group being studied.
sample: a subset of the population used for analysis.

estimation

point estimation: provides a single value estimate of a population parameter.
interval estimation: offers a range of values (confidence interval) within which the parameter likely lies.
confidence intervals: indicate the reliability of an estimate.

hypothesis testing

hypothesis testing is a method that compares two opposite assumptions about a population and uses data from a sample to determine which assumption is more likely to be true.

null and alternative hypotheses: the null hypothesis assumes no effect or relationship, while the alternative suggests otherwise.
type i and type ii errors: type i error is rejecting a true null hypothesis, while type ii is failing to reject a false null hypothesis.
p-values: measure the probability of obtaining the observed results under the null hypothesis.
t-tests and z-tests: compare means to assess statistical significance.
anova (analysis of variance): compares means across multiple groups to determine if they differ significantly.
chi-square tests: assess the association between categorical variables.

covariance and correlation

covariance	correlation
covariance measures the degree to which two variables change together. cov(x,y) = \frac{\sum(x_i-\overline{x})(y_i - \overline{y})}{n}	correlation shows how strongly and in which direction two variables are related. its coefficient ranges from -1 to 1, where positive means they move together and negative means they move oppositely. pearson's correlation coefficient is given by: \rho(x, y) = \frac{cov(x,y)}{\sigma_x \sigma_y}

pearson correlation coefficient: measures linear relationship strength between two variables.
spearman rank correlation: assesses the strength and direction of the monotonic relationship between variables.

visualization techniques

histograms: show data distribution.
box plots: highlight data spread and potential outliers.
scatter plots: illustrate relationships between variables.

regression analysis

understanding relationships between variables is important in machine learning.

simple linear regression: models the relationship between two variables.
multiple linear regression: extends to multiple predictors.
assumptions of linear regression: linearity, independence, homoscedasticity, normality.
interpretation of regression coefficients: explains predictor influence on the response variable.
model evaluation metrics: r-squared, adjusted r-squared, rmse.

probability theory

probability theory forms the backbone of statistical inference, aiding in quantifying uncertainty and making predictions based on data.

basic concepts

random variables: variables with random outcomes.
probability distributions: describe the likelihood of different outcomes.
law of large numbers: states that as the sample size increases, the sample mean approaches the population mean.
central limit theorem: indicates that the distribution of sample means approximates a normal distribution as the sample size grows, regardless of the population's distribution.

common probability distributions

binomial distribution: represents the number of successes in a fixed number of trials.
poisson distribution: describes the number of events occurring within a fixed interval.
normal distribution: characterizes continuous data symmetrically distributed around the mean.

bayesian statistics

bayesian statistics combine prior knowledge (what we already believe) with new data (current evidence) to update our understanding.

bayes' theorem is a fundamental concept in probability theory that relates conditional probabilities. it is named after the reverend thomas bayes, who first introduced the theorem. bayes' theorem is a mathematical formula that provides a way to update probabilities based on new evidence.

formula:

p(a \mid b) = \frac{p(b \mid a) \times p(a)}{p(b)}

where

p(a∣b): the probability of event a given that event b has occurred (posterior probability).
p(b∣a): the probability of event b given that event a has occurred (likelihood).
p(a): the probability of event a occurring (prior probability).
p(b): the probability of event b occurring.

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