> For the complete documentation index, see [llms.txt](https://alxfed.gitbook.io/notebook/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://alxfed.gitbook.io/notebook/animal-speak/scikit-learn.md). # Scikit-Learn Borrowed mostly from here:\ \ and here: ## **data -** a **set** of objects or **individuals** (in Stats-speak) each of which has multiple properties (Human), **variables** (Stats-speak) or **features** (Scikit-speak).\ \ **Example:** human census is **data**, the set consists of humans-*individuals* , each human has height, weight etc.etc. - the **features** which are also called "attributes" (because of the census, duh!). Notice that a human must have height and weight, (s)he can not *not* have them. The complete set of data is sometimes called **population** (as opposed to sample which is a sub-set of it). a subset of data is called **sample** (see below). It is just what I've just said a subset picked out of the data according to a criterion (including a 'random' selection). **Human language**: If you are a student of Newtonian dynamics or Mechanics or, God forbid, Electrodynamics - you need to wrap your head around it here. Everything is totally static, there is only a set of pieces on the chessboard and of course all the Animal-speak is created for the sole purpose of being able to joggle the super-expensive to acquire numbers and answers to questions as the 'researcher' wants in order to obtain the desired 'prediction' that will sell well. Sorry for being cynical but that's what it is and nothing more, - the Census data collection as any other 'field research' required in the past a lot of money. Now you can probably surveil each human in an industrialized country in real time and work with that 'data'. :) ## sample - a subset of data from the **population**/(all of) **data** selected by a predefined procedure (including a 'random' selection). The elements of a sample are known as "sample points", "sampling units" or "observations".\ \ **Example**: a subset of humans in the same age group taken from the census data (the population). {% hint style="info" %} **Descriptive statistics** is the branch of statistics that includes methods for organizing and summarizing data. **Inferential statistics** is the branch of statistics that involves generalizing from a sample to the population from which the sample was selected and assessing the reliability of such generalizations. {% endhint %} ## n\_samples - is the pure Scikit-speak, it means a number of individual "data points" in the data, which is a `n_samples` \* `n_features` sized array with **key** (SQL-speak) `data` . ## **feature -** the Scikit-speak for a **variable** of Stat-speak. It's a variable in a very broad sense of the word, because it can be an object representing a complex phenomenon (like a histogram of a noise), but it's a variable (or at maximum a 'representation of a variable') none the less. They also use the word attribute *for the same meaning* here and there, because... they want to. Seriously, there is no need in this meaningless (in this context) word "feature". {% hint style="info" %} In [machine learning](https://en.wikipedia.org/wiki/Machine_learning) and [pattern recognition](https://en.wikipedia.org/wiki/Pattern_recognition), a **feature** is an individual measurable property or characteristic of a phenomenon being observed. Choosing informative, discriminating and independent features is a crucial step for effective algorithms in [pattern recognition](https://en.wikipedia.org/wiki/Pattern_recognition), [classification](https://en.wikipedia.org/wiki/Classification_\(machine_learning\)) and [regression](https://en.wikipedia.org/wiki/Regression_analysis). Features are usually numeric, but structural features such as [strings](https://en.wikipedia.org/wiki/String_\(computer_science\)) and [graphs](https://en.wikipedia.org/wiki/Graph_\(discrete_mathematics\)) are used in [syntactic pattern recognition](https://en.wikipedia.org/wiki/Syntactic_pattern_recognition). The concept of "feature" is related to that of [explanatory variable](https://en.wikipedia.org/wiki/Explanatory_variable) used in [statistical](https://en.wikipedia.org/wiki/Statistics) techniques such as [linear regression](https://en.wikipedia.org/wiki/Linear_regression). {% endhint %} ## n\_features - number of **variables** that every individual data points depends on, in Scikit -speak they interchangeably use the words **features** and **attributes** to describe the content of the line in the array of data representing an individual data point. ## **target** - The word that is constantly used in all the functions related to **regression**. \ **The synonyms** of it are: "label", "class" and even "name of object" (in the sense "apple" or "orange"). It can actually be multidimensional, but typically is just 1D.\ **The human meaning** of it is: it's the thing you are trying to predict with the help of the "regression analysis". It may be one of the variables the data points depend on or it may be totally different, from outside the data table, then they call it an **external\_variable** assigned to each of the individual data points separately and because of that called **label**. {% hint style="info" %} [Supervised learning](https://scikit-learn.org/stable/supervised_learning.html#supervised-learning) consists in learning the link between two datasets: the observed data `X` and an external variable `y` that we are trying to predict, usually called “target” or “labels”. Most often, `y` is a **1D** array of length `n_samples`. All supervised [estimators](https://en.wikipedia.org/wiki/Estimator) in scikit-learn implement a `fit(X, y)` method to fit the model and a `predict(X)`method that, given unlabeled observations `X`, returns the predicted labels `y.` {% endhint %} {% embed url="" %} ## n\_targets - the other dimension of 'output' matrix y. ## dataset loaders, fetchers - loaders and fetchers functions return a dictionary-like object holding at least two items: an array of shape `n_samples` \* `n_features` with key (a Python dictionary "key", not an SQL key) `data` and a ***numpy*** array of length `n_samples`, containing the **target** values, with key (a Python dictionary "key", not an SQL key) `target`. \
## dataset generators can be used to generate controlled synthetic datasets. {% hint style="info" %} These functions return a tuple `(X, y)` consisting of a `n_samples` \* `n_features` **numpy** array `X` and an array of length `n_samples` containing the targets `y`. {% endhint %} Notice that for some inexplicable reason (probably because "it just happened so") this is a tuple while the loaders and fetchers return a dictionary... --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://alxfed.gitbook.io/notebook/animal-speak/scikit-learn.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.