SIOC Post profile for "AI3:::Adaptive Information"
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primary topic: CWPK #65: scikit-learn Basics and Initial Encoding
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CWPK #65: scikit-learn Basics and Initial Encoding
type: Post
has container: weblog
has creator: Mike Bergman
maker: Mike Bergman
created: 2020-11-23T15:38:35Z
content: Welcome to a Lengthy Installment on Feature Engineering We devote the next two installments of Cooking with Python and KBpedia to the venerable Python machine learning package, scikit-learn. Also known as 'sklearn', this package offers a wealth of classic machine learning methods and utilities, along with abilities to construct machine learning pipelines and collect and present results via a rich set of statistical measures. Though it may be a distinction without a difference, this installment marks our transition in this Part VI to packages devoted to machine learning. Though our earlier discussions of gensim and spaCy highlighted packages that employ much machine learning in their capabilities, their focus is not strictly machine learning in the same way as our remaining packages. After two installments on classic machine learning with scikit-learn, the remaining five installments in this Part VI are dedicated to deep learning with knowledge graphs. In this installment we will set-up scikit-learn and prep a master data file as input to it (and to subsequent packages). The efforts we detail in this installment -- one of the longest in our CWPK series -- falls into the discipline known as 'feature engineering', the process of extracting and crafting numeric representations staged properly for machine learning. In this installment, we load all of the necessary pieces and then proceed through a pipeline of data wrangling steps to create our vector file of numeric representations directly usable by the machine learners. This information then prepares us for the following installment, where we will set up our experimental training sets, establish a 'gold standard' by which we calculate statistical performance, and begin some initial classification. We also set up our framework for reporting and comparing results in all of our machine learning installments. Install scikit-learn Because of our initial use of Anaconda, we already have scikit-learn installed. We can confirm…
encoded: Welcome to a Lengthy Installment on Feature Engineering We devote the next two installments of Cooking with Python and KBpedia to the venerable Python machine learning package, scikit-learn. Also known as ‘sklearn’, this package offers a wealth of classic machine learning methods and utilities, along with abilities to construct machine learning pipelines and collect and present results via a rich set of statistical measures. Though it may be a distinction without a difference, this installment marks our transition in this Part VI to packages devoted to machine learning. Though our earlier discussions of gensim and spaCy highlighted packages that employ much machine learning in their capabilities, their focus is not strictly machine learning in the same way as our remaining packages. After two installments on classic machine learning with scikit-learn, the remaining five installments in this Part VI are dedicated to deep learning with knowledge graphs. In this installment we will set-up scikit-learn and prep a master data file as input to it (and to subsequent packages). The efforts we detail in this installment — one of the longest in our CWPK series — falls into the discipline known as ‘feature engineering‘, the process of extracting and crafting numeric representations staged properly for machine learning. In this installment, we load all of the necessary pieces and then proceed through a pipeline of data wrangling steps to create our vector file of numeric representations directly usable by the machine learners. This information then prepares us for the following installment, where we will set up our experimental training sets, establish a ‘gold standard‘ by which we calculate statistical performance, and begin some initial classification. We also set up our framework for reporting and comparing results in all of our machine learning installments. Install scikit-learn Because of our initial use of Anaconda, we already have scikit-learn installed. We can confirm t…
topic: CWPK
topic: KBpedia
topic: Semantic Web Tools
links to: Cooking with Python and KBpedia
links to: Python
links to: scikit-learn
links to: machine learning
links to: pipelines
links to: gensim
links to: spaCy
links to: deep learning
links to: knowledge graphs
links to: feature engineering
links to: gold standard
links to: Anaconda
links to: cb1-1
links to: API documentation
links to: pandas
links to: PyTorch
links to: SciPy
links to: NumPy
links to: libSVM
links to: CSV
links to: Excel
links to: JSON
links to: SQL
links to: user guide
links to: related projects
links to: cb2-1
links to: cb2-2
links to: cb2-3
links to: cb2-4
links to: cb2-5
links to: cb3-1
links to: cb3-2
links to: cb3-3
links to: cb3-4
links to: cb3-5
links to: cb4-1
links to: cb4-2
links to: cb4-3
links to: cb4-4
links to: cb4-5
links to: CWPK #48
links to: CWPK #44
links to: cb5-1
links to: cb5-2
links to: cb5-3
links to: cb5-4
links to: cb5-5
links to: cb5-6
links to: cb5-7
links to: cb5-8
links to: cb5-9
links to: cb5-10
links to: cb5-11
links to: cb5-12
links to: cb5-13
links to: cb5-14
links to: cb5-15
links to: cb5-16
links to: cb5-17
links to: cb5-18
links to: cb5-19
links to: cb5-20
links to: cb5-21
links to: cb5-22
links to: cb5-23
links to: cb5-24
links to: cb5-25
links to: cb5-26

Mike Bergman
type: User

Mike Bergman
type: Person

Cooking with Python and KBpedia
label: CWPK listing

Atom
label: GitHub

tablr

EmEditor

tablr JS issue

join and merge routines

concatenations

inner or outer

CWPK #63

CWPK #56

principal component analysis

grid search

pandas and sklearn

sklearn-pandas

ColumnTransformer

heterogeneous data
label: ColumnTransformer

mixed data types

transform multiple columns

pretty good overview

pipelines with custom transformers

Category encoding

LabelEncoder

OneHotEncoder

get_dummies

str.get_dummies

useful utilities
label: package documentation

pandas utilities

CountVectorizer

HashingVectorizer

TfidfTransformer

TfidfVectorizer

hash trick

TF/IDF

fit

transformed

fit-transform

scipy.sparse.csr_matrix

data wrangling

Texthero

NLTK

TextBlob

doc2vec

50 tips for sklearn

Scikit-learn Tutorial: Machine Learning in Python

skorch

Category Encoders

preprocessing module

Model Evaluation

pandas and scikit-learn

Lesson 4. Introduction to List Comprehensions in Python: Write More Efficient Loops

Curse of Dimensionality

KBpedia

code listing covering the series

direct download

notify me