本篇系列文章，透過Matlab 官方線上課程，進行重點節錄。

1. 表格資料處理

The readtable function creates a table in MATLAB from a data file.

allStats = readtable("bballStats.txt")

playerInfo = readtable("bballPlayers.txt");

The categorical function creates a categorical array from data.

positions = ["G","G-F","F-G","F","F-C","C-F","C"]; 

playerInfo.pos = categorical(playerInfo.pos,positions);

Assigning the empty array removes rows or columns. The rmmissing function removes any row with missing or undefined elements.

allStats(19:end) = []; 

playerInfo = rmmissing(playerInfo);

The groupsummary function calculates statistics grouped according to a grouping variable.

playerStats = groupsummary(allStats,"playerID","sum");

The innerjoin function merges two tables, retaining only the common key variable observations.

data = innerjoin(playerInfo,playerStats);

2. 視覺化與特徵提取

The boxplot function can create separate box plots based on a grouping variable.

boxplot(data.height,data.pos)

You can use indexing and element-wise division to scale variables in a table.

data{:,8:22} = data{:,8:22}./data.minutes; 

data.minutes = data.minutes./data.GP;

You can use the gscatter function to create a grouped scatter plot.

gscatter(data.rebounds,data.points,data.pos)

3. 訓練模式（KNN model）

The fitcknn function fits a k-nearest neighbors classification model.

knnmodel = fitcknn(data,"pos");

You can use property name-value pairs to modify model options.

knnmodel = fitcknn(data, "pos", "NumNeighbors", 5, "Standardize", true);

You can calculate the misclassification rate for a data set using the loss function.

mdlLoss = loss(knnmodel,dataTest)

output: mdlLoss = 0.4085

The predict function uses a classification model to predict classes for observations.

predPos = predict(knnmodel, dataTest);

You can use the confusionchart function to visually compare true classes and predicted classes.

confusionchart(data.pos, predPos);