3 Studi Kasus Machine Learning dan Data Mining

STUDI KASUS

MACHINE LEARNING

DAN DATA MINING

Kelas Logistik dan Jadwal

• _{36 Pertemuan}

• _{Software yang digunakan}

– _{Python 3}

– _{TensorFlow (TF) + Keras}

Daftar isi

• _{Terminologi AI, Machine Learning dan data mining}

• _{Learning data untuk model}

• _{Tipe-tipe Tugas Belajar (Learning Tasks)}

• _{Pendefinisian tugas belajar (Learning Task)}

• _{Contoh-contoh kasus machine Learning}

Terminologi

• _Sinonim

– Artificial Intelligence

– Machine Learning

– Data mining

– _{Pattern recognition}

– Probability and Statistics

– Information theory

– Numerical optimization

– Computational complexity theory

– Control theory (adaptive)

Machine Learning,Statistics dan Data Mining

• _{Differences in terminology:}

– _{Ridge regression = weight-decay} – _{Fitting = learning}

– Held-out data = test data

• _{The emphasis is very different:}

– _{A good piece of statistics: Clever proof that a} relatively simple estimation procedure is

asymptotically unbiased.

– _{A good piece of machine learning: Demonstration} that a complicated algorithm produces impressive results on a specific task.

• _{Data-mining: Using machine learning}

techniques on very large databases.

“Learning” Data

• _{Learning general models dari a data of particular}

examples

• Data tersedia banyak dan murah(data warehouses, data marts); knowledge mahal dan jarang.

• _{Contoh dalam retail: Customer transactions to consumer}

behavior:

People who bought “Da Vinci Code” also bought “The Five People You Meet in Heaven” (www.amazon.com)

• Pembuatan model yang a good and useful approximation

Tipe-tipe Tugas Belajar (Learning Tasks)

• _Association

• _{Supervised learning}

– _{Learn to predict output when given an input vector}

• _{Reinforcement learning}

– _{Learn action to maximize payoff} • _{Payoff is often delayed}

• Exploration vs. exploitation

• Online setting

• _{Unsupervised learning}

– _{Create an internal representation of the input e.g. form}

clusters; extract features

• How do we know if a representation is good?

– _{Big datasets do not come with labels.}

Learning Associations

• _{Basket analysis:}

P (Y | X ) probability that somebody who buys X

also buys Y where X and Y are products/services.

Classification

• _{Example: Credit}

scoring

• _{Differentiating}

between low-risk and

high-risk customers from their income and

savings

Discriminant: IF income > θ₁ AND savings > θ₂

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Aplikasi-aplikasi Classification

• _{Aka Pattern recognition}

• _{Face recognition: Pose, lighting, occlusion (glasses,} beard), make-up, hair style

• Character recognition: Different handwriting styles. • _{Speech recognition: Temporal dependency.}

– Use of a dictionary or the syntax of the language.

– _{Sensor fusion: Combine multiple modalities; eg, visual (lip}

image) and acoustic for speech

• _{Medical diagnosis: From symptoms to illnesses}

Face Recognition

Training examples of a person

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Penggunaan Supervised

Learning

• _{Prediction of future cases: Use the rule to predict}

the output for future inputs

• _{Knowledge extraction: The rule is easy to}

understand

• _{Compression: The rule is simpler than the data it}

explains

• _{Outlier detection: Exceptions that are not covered}

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Penggunaan Unsupervised Learning

• _{Learning “what normally happens”}

• _{Clustering: Grouping similar instances}

• _{Example applications}

– _{Customer segmentation in CRM (customer relationship}

management)

– _{Image compression: Color quantization}

Displaying the structure of a set of documents

Contoh: Cancer Diagnosis

• _{Application: automatic disease detection}

• _{Importance: this is modern/future medical}

diagnosis.

• _{Prediction goal: Based on past patients, predict}

whether you have the disease

• _{Data: Past patients with and without the disease} • _{Target: Cancer or no-cancer}

• _{Features: Concentrations of various proteins in}

your blood

Contoh: Zipcodes

• _{Application: automatic zipcode recognition}

• _{Importance: this is modern/future delivery of small}

goods.

• _{Goal: Based on your handwritten digits, predict}

what they are and use them to route mail

• _{Data: Black-and-white pixel values} • _{Target: Which digit}

• _{Features: ?}

What makes a 2?

Contoh: Google

• _{Application: automatic ad selection}

• _{Importance: this is modern/future advertising.}

• _{Prediction goal: Based on your search query,}

predict which ads you might be interested in

• _{Data: Past queries}

• _{Target: Whether the ad was clicked} • _{Features: ?}

Contoh: Call Centers

• _{Application: automatic call routing}

• _{Importance: this is modern/future customer}

service.

• _{Prediction goal: Based on your speech recording,}

predict which words you said

• _{Data: Past recordings of various people} • _{Target: Which word was intended}

• _{Features: ?}

Contoh: Stock Market

• _{Application: automatic program trading}

• _{Importance: this is modern/future finance.}

• _{Prediction goal: Based on past patterns, predict}

whether the stock will go up

• _{Data: Past stock prices} • _{Target: Up or down}

• _{Features: ?}

Contoh :Web-based

• The web contains a lot of data. Tasks with very big datasets often use machine learning

– especially if the data is noisy or non-stationary.

• Spam filtering, fraud detection:

– The enemy adapts so we must adapt too.

• Recommendation systems:

– Lots of noisy data. Million dollar prize!

• Information retrieval:

– Find documents or images with similar content.

What is a Learning Problem?

• Learning involves performance improving

– _{at some task T} – with experience E

– _{evaluated in terms of performance measure}

P

• Example: learn to play checkers

– _{Task T: playing checkers}

– _{Experience E: playing against itself} – _{Performance P: percent of games won} • What exactly should be learned?

– How might this be represented?

– _{What specific algorithm should be used?}

Develop methods, techniques and tools for building intelligent

learning machines, that can solve the problem in combination with an available data set of training

examples.

When a learning machine improves its performance at a given task over time, without reprogramming, it can be said to have learned

something.

Pendefinisian tugas belajar (Learning

Task)

Improve on task, T, with respect to

performance metric, P, based on experience, E.

T: Playing checkers

P: Percentage of games won against an arbitrary opponent E: Playing practice games against itself

T: Recognizing hand-written words

P: Percentage of words correctly classified

E: Database of human-labeled images of handwritten words

T: Driving on four-lane highways using vision sensors

P: Average distance traveled before a human-judged error

E: A sequence of images and steering commands recorded while observing a human driver.

Desain sebuah Learning System

• Pilih : training experience

• _{Pilih : what is too be learned, i.e. the target function.} • Pilih: how to represent the target function.

• _{Pilih: a learning algorithm to infer the target function}

from the experience.

Environment/ Experience

Learner

Knowledge

Komponen-komponen sebuah Learning Problem

• Task: the behavior or task that’s being improved, e.g.

classification, object recognition, acting in an environment.

• Data: the experiences that are being used to improve

performance in the task.

• Measure of improvements: How can the improvement be measured? Examples:

– Provide more accurate solutions (e.g. increasing the accuracy in prediction)

– Cover a wider range of problems

– Obtain answers more economically (e.g. improved speed)

– Simplify codified knowledge

– New skills that were not presented initially

What Experience E to Use?

• Direct or indirect?

– Direct: feedback on individual moves

– Indirect: feedback on a sequence of moves

• e.g., whether win or not • Teacher or not?

– Teacher selects board states

• Tailored learning

• Can be more efficient

– Learner selects board states

• No teacher • Questions

– Is training experience representative of performance goal?

– Does training experience represent distribution of outcomes in world?

What Exactly Should be Learned?

• Playing checkers:

– Alternating moves with well-defined rules

– Choose moves using some function

– Call this function the Target Function

• Target function (TF): function to be learned during a learning process

– ChooseMove: Board  Move

– ChooseMove is difficult to learn, e.g., with indirect training examples

 A key to successful learning is to choose appropriate target function:

 Strategy: reduce learning to search for TF

• Alternative TF for checkers:

– V : Board  R

– Measure “quality” of the board state

– Generate all moves

• choose move with largest value

A Possible Target Function

V

For

Checkers

• _{In checkers, know all legal moves}

– _{From these, choose best move in any situation}

• _{Possible V function for checkers:}

– _{if b is a final board state that is win, then V(b) = 100} – _{if b is a final board state that is loss, then V(b) =} -100

– _{if b is a final board state that is draw, then V(b) = 0}

– _{if b is a not a final state in the game, then V(b) = V(b}_{), where b} _{is the}

best final board state that can be achieved starting from b and playing optimally until the end of the game

• _{This gives correct values, but is not operational}

– _{So may have to find good approximation to V} – _{Call this approximation V}

How Might Target Function be Represented?

• Many possibilities (subject of course)

– As collection of rules ?

– _{As neural network ?}

– As polynomial function of board features ?

• Example of linear function of board features:

– w0+ w1bp(b) + w2rp(b) + w3bk(b)+w4rk(b)+w5bt(b)+w6rt(b)

• bp(b) : number of black pieces on board b

• rp(b) : number of red pieces on b

• bk(b) : number of black kings on b

• rk(b) : number of red kings on b

• bt(b) : number of red pieces threatened by black (i.e., which can be taken on black's next turn)

• rt(b) : number of black pieces threatened by red

• Generally, the more expressive the representation, the more difficult it is to estimate

Inductive and Deductive Learning

• _{Inductive Learning: Reasoning from a set of}

examples to produce a general rules. The rules

should be applicable to new examples, but there is no guarantee that the result will be correct.

• _{Deductive Learning: Reasoning from a set of}

known facts and rules to produce additional rules that are guaranteed to be true.

Assessment of Learning Algorithms

• _{The most common criteria for learning algorithms}

assessments are:

– Accuracy (e.g. percentages of correctly classified +’s and –’s)

– Efficiency (e.g. examples needed, computational tractability)

– _{Robustness (e.g. against noise, against incompleteness)} – Special requirements (e.g. incrementality, concept drift)

– Concept complexity (e.g. representational issues – examples & bookkeeping)

– Transparency (e.g. comprehensibility for the human user)

Data mining dan algorithms

• Data Mining

– The desired outcome from data mining is to create a model from a given dataset that can have its insights generalized to similar datasets. A real-world example of a successful data mining application can be seen

in automatic fraud detection from banks and credit institutions.

– Data mining is the process of discovering predictive information from the

analysis of large databases. For a data scientist, data mining can be a vague and daunting task – it requires a diverse set of skills and knowledge of many data mining techniques to take raw data and successfully get insights from it.  You’ll want to understand the foundations of statistics, and 

Teknik-teknik Data mining

• Regression – Estimating the relationships between variables by optimizing the reduction of error.

• Classification – Identifying what category an object belongs to. An example is classifying email as spam or legitimate, or looking at a person’s credit score and approving or denying a loan request.

• Cluster Analysis – Finding natural groupings of data objects based upon the known

characteristics of that data. An example could be seen in marketing, where analysis can reveal customer groupings with unique behavior – which could be applied in business strategy

decisions.

• Association and Correlation Analysis – Looking to see if there are unique relationships between variables that are not immediately obvious. An example would be the famous case of beer and diapers: men who bought diapers at the end of the week were much more likely to buy beer, so stores placed them close to each other to increase sales.

Contoh pemakaian panda pada model regresi pada Python

• _{Menghitung relasi linear diantara variabel yang}

tersedia, menggunakan data dari 

House Sales in King’s County data set from

Kaggle.

Python script

import pandas as pd

import matplotlib.pyplot as plt import numpy as np

import scipy.stats as stats import seaborn as sns

from matplotlib import rcParams

Tampilan Hasil

• _{df.describe()}