Introduction to Machine Learning and Data Analysis | Realcode4you

What is machine learning?

1- A set of tools and methods that attempt to extract insight from a record of the observable world and infer patterns.

2- Studying and understanding a phenomenon

• Make observations and collect the relevant data

• Model the underlying patterns

• Use the model to inform our understanding of the phenomenon

• Make predictions!!!

3- An important feature of any ML method is its ability to learn and improve with experience, i.e. both existing and new data.

ML attempts to answer:

How does learning performance vary with the number of training examples?

Which learning algorithms are most appropriate for various types of learning tasks?

ML draws on concepts and results from:

• Statistics

• Artificial intelligence

• Philosophy

• Information theory

• Biology

• Cognitive science

• Control theory

Introduction to Machine Learning

Supervised Learning

Infers a function that maps set of inputs (features, predictors, covariates, independent variables) to an output (response, target, outcome, dependent variable) from input/output pairs.

The function is inferred from training examples, which are mapped to new examples.

Goals:

• Accurately predict unseen cases, i.e. test cases (primary)

• Understand the relationship between inputs and output (secondary)

Two sub-categories:

• Regression – a continuous outcome

• Classification – a categorical/qualitative outcome

Unsupervised Learning

1. No distinctions between input(s) and output within a data set.

2. Attempts to uncover the underlying structure or pattern within a data set.

3. Can lead to testable hypotheses.

4. Difficult to know how well you have done.

Two sub-categories:

• Dimension reduction – visualisation of multi-dimensional data in lower dimensions, 2-D and 3-D.

• Clustering – grouping of objects based on some similarity measures.

Introduction to Data Analysis

What is statistics?

• Statistics allow us to learn from our data.

• Data are numbers with context.

• Data contains information about some group of individuals.

• A characteristic of an individual is referred to as a variable.

Data Types

Two main types of data:

Qualitative (categorical) – variables that represent qualities and cannot be measured.

• Nominal – characteristics have no order, e.g. eye colour, gender (male/female).

• Ordinal – characteristics that are intrinsically ordered, e.g. educational attainment (primary, secondary, tertiary).

Quantitative (numerical)

• Discrete – able to take only certain distinct values within an allowable range. The allowable range maybe finite or infinite. For example, outcome of a dice roll, and number of students.

• Continuous – data measured on a scale, able to take on any values within an allowable range which maybe finite or infinite, e.g. body mass, height.

Exploratory Data Analysis (EDA)

• EDA is the process of describing the data and summarising the main characteristics.

• Provides some insight into the behaviour of the data.

• A critical aspect of EDA is outlier detection.

• Describe graphically and numerically.

Describing Qualitative and Discrete Data

• Qualitative and discrete (finite) data are typically expressed as count data.

• For a better perspective, counts are often expressed as a percentage of the total.

Describing Quantitative Data

Three aspects are addressed

• Measure of Centre: describes how data cluster around a particular value.

• Measure of Spread: describes the dispersion/variability of data

• Measure of Shape: describes the distribution (or pattern) of data.

Measure of Centre (Central Tendency)

Measure of Spread (Dispersion)

Example :

Percentiles and 5-Number Summary

Example :

Measure of Shape

Two measures:

1.Skewness – a measure of symmetry, or more precisely, the lack of symmetry. A distribution is symmetric if it looks the same to the left and right of the centre point.

2.Kurtosis – is a measure tailed-ness relative to a normal distribution.

Skewed Distributions

Excess Kurtosis

Data Quality Issues

What are the issues to consider?

Data entry errors

• Values outside of expected range(s)

Missing values

• Noted as NA in R

• >20% is not good.

Outliers

• Certain descriptive statistics and modelling are sensitive to them

• Can lead to bias estimates and potentially incorrect findings

Handling Missing Values

Approach 1: Drop any features with missing values

• Typically not recommended

• Depends on the % of missing values

• The is.na(.)  command in R can be used to check for missing values

Approach 2: Analyse complete cases only

• Use the na.omit(.) command in R

• Important to note % of cases removed

Approach 3: Impute the missing values

• Mean imputation, regression imputation, K-NN and etc.

• Recommended only for continuous data

Detecting Outliers

• Check the range, i.e. min to max

• Visualise the data, e.g. histograms, boxplots, etc.

• Use thresholds, e.g. (Q_1, Q_3) ±1.5 × IQR, z-scores outside of ±3, etc.

Handling Outliers

Approach 1: Remove them

• Typically not recommended, in particular with smaller datasets

• Somewhat acceptable for large datasets

Approach 2: Investigate the source and find out why  this has happened

Approach 3: Non-linear data transformation

• Square-root and log-transformation for right skewed data.