Analysing wine quality, F#. https://www.meetup.com/FSharpHelsinki/events/242919339/ Copy: https://docs.google.com/presentation/d/1JnxOR9ysNSLfBqWTjrgwTVrKHNBe_L73ZSvJt3A_3DY/edit?usp=sharing

1. Machine-learning with
Accord.NET: Wine-quality
This example is based on the Wine Quality dataset from the
University of California Irvine Machine Learning Repository:
https://archive.ics.uci.edu/ml/datasets/Wine+Quality

2. Machine-Learning
• Current cloud providers (Microsoft, Amazon, Google, …)
have interest to sell computing power as API
• Machine-learning takes a lot of computing power
They have the interest to make it the next buzz-word.
They made cloud a buzz-word, they can do it again.
e.g. https://azure.microsoft.com/en-in/services/machine-learning/
However, this time we won’t use any APIs, but an open source tool called Accord.NET.

3. A case for machine learning
 We have some existing sample data.
 We want to estimate a variable known in the sample data,
but not in the real life.
 We expect that the real results will follow the sample data.
Randomize the sample data rows order and split it to two
parts:
1) Training set
• Used to find the correct model.
2) Model evaluation set
• Used to verify that the model works to data outside the trained samples

4. A sample dataset:
Wine quality
There is just a parameters of wines and a people
voted quality from 0 to 10:
https://archive.ics.uci.edu/ml/machine-learning-
databases/wine-quality/winequality-red.csv
Can we estimate a quality of non-listed wine based
on the features we know?
fixed acidity
volatile
acidity citric acid residual sugar chlorides
free sulfur
dioxide
total sulfur
dioxide density pH sulphates alcohol quality
7.4 0.7 0 1.9 0.076 11 34 0.9978 3.51 0.56 9.4 5
7.3 0.65 0 1.2 0.065 15 21 0.9946 3.39 0.47 10 7

5. (Linear) Regression
Creating a linear regression over
one feature is relatively simple.
y = k x + b
The dataset has a large amount of
wines, with different alcohol levels
and qualities.
But the dataset has 10 other
features also, so how to make a
regression over combined 11
variables? Takes forever…?
y = k1 x1 + k2 x2 + … + kn xn + b Original picture from: http://brandewinder.com/2016/08/06/gradient-boosting-part-1/

6. Accord .NET cancer example
Age Smokes Had cancer
55 0 FALSE
28 0 FALSE
65 1 FALSE
46 0 TRUE
86 1 TRUE
56 1 TRUE
85 0 FALSE
33 0 FALSE
21 1 FALSE
42 1 TRUE
Feature Odd ratio
Age 1.02
Smoking 5.86
Calculation y(x0, x1) =
0.0206451183100222*x0
+ 1.76788931343272*x1
+ -2.45774643623285
Decide()
http://fssnip.net/7Sz

7. Decision trees
Instead of combining slopes, create a combination
of feature-condition-stumps.
Estimating a few (discrete) categories based on
combination of decision nodes.
What method should I choose?
http://scikit-learn.org/stable/_static/ml_map.png
PH > 3.5
Alcohol > 10.6
Manual example and theory:
http://brandewinder.com/2016/08/06/gradient-boosting-part-1/
http://fssnip.net/7Tz
Figure has just 2 stumps, but real life AI can
generate huge trees.

8. Use-case: Quality for our event’s wine from Alko
https://www.alko.fi/tuotteet/455518/Frontera-Cabernet-Sauvignon-2016-hanapakkaus
Data from Alko analysis laboratory, wine entry L2BIBS34016:
In Finnish In English
Alk-% 12,01 Alcohol 12.01
Sokeri 3,5 g/l Sugar 3.5
Haihtuvat hapot 0,5 g/l Volatile acidity 0.5
Kokonaisrikki 96 mg/l Total sulfur 96
Vapaa rikki 36 mg/l Free sulfur 36
Sitruunahappo 0,045 g/l Citric acid 0.045
• This sample is from Chile and the sample data is from Italy, so our algorithm has to be able to
work outside the dataset.
• Parameter mismatch:
1) Convert parameters,
2) Remove parameter from learning process
 Measure the error, effect to model quality
We don’t have Mean
Fixed acidity 8.32 g/l
Chlorides 0.087 g/l
Density 0.9967 g/l
pH 3.31
Sulphates 0.66 g/l
Extra data Known
Total acids 4.62 g/l
Extract 29.7
Density “medium”
Cabernet Sauvignong
(Alko provided the data I asked by email)