timbl(1) | General Commands Manual | timbl(1) |

# NAME¶

timbl - Tilburg Memory Based Learner

# SYNOPSIS¶

timbl [options]

timbl -f data-file -t test‐file

# DESCRIPTION¶

TiMBL is an open source software package implementing several memory‐based learning algorithms, among which IB1‐IG, an implementation of k‐nearest neighbor classification with feature weighting suitable for symbolic feature spaces, and IGTree, a decision‐tree approximation of IB1‐IG. All implemented algorithms have in common that they store some representation of the training set explicitly in memory. During testing, new cases are classified by extrapolation from the most similar stored cases.

# OPTIONS¶

**-a** <n> or **-a** <string>

Possible values are:

**0** or **IB**

the IB1 (k‐NN) algorithm (default)

**1** or **IGTREE**

a decision‐tree‐based approximation of IB1

**2** or **TRIBL**

a hybrid of IB1 and IGTREE

**3** or **IB2**

an incremental editing version of IB1

**4** or **TRIBL2**

a non‐parameteric version of TRIBL

**-b** n

**-B** n

**--Beam**=<n>

**--clones**=<n>

**-c** n

**+D**

**--Diversify**

**-d** val

Z : equal weights to all (default)

ID : Inverse Distance

IL : Inverse Linear

ED:a : Exponential Decay with factor a (no whitespace!)

ED:a:b : Exponential Decay with factor a and b (no whitespace!)

**-e** n

**-f** file

**-F** format

**-G** normalization

Supported normalizations are:

**Probability** or **0**

normalize between 0 and 1

**addFactor**:<f> or **1**:<f>

add f to all possible targets, then normalize between 0 and 1 (default f=1.0).

**logProbability** or **2**

Add 1 to the target Weight, take the 10Log and then normalize between 0 and 1

**+H** or **-H**

**-i** file

**-I** file

**-k** n

**-L** n

**-l** n

**-m** string

The format is : GlobalMetric:MetricRange:MetricRange

e.g.: mO:N3:I2,5-7

C: cosine distance. (Global only. numeric features implied)

D: dot product. (Global only. numeric features implied)

DC: Dice coefficient

O: weighted overlap (default)

E: Euclidian distance

L: Levenshtein distance

M: modified value difference

J: Jeffrey divergence

S: Jensen‐Shannon divergence

N: numeric values

I: Ignore named values

**--matrixin**=file

**--matrixout**=file

**-n** file

**-M** n

**-N** n

**-o** s

**--occurrences**=<value>

**train**,

**test**or

**both**

**-O** path

**-p** n

**-P** path

**-q** n

**-R** n

**-s**

**-s0**

**-T** n

**-t** file

**-t** leave_one_out

**-t** cross_validate

**-t** @file

**--Treeorder =value** n

DO: none

GRO: using GainRatio

IGO: using InformationGain

1/V: using 1/# of Values

G/V: using GainRatio/# of Valuess

I/V: using InfoGain/# of Valuess

X2O: using X‐square

X/V: using X‐square/# of Values

SVO: using Shared Variance

S/V: using Shared Variance/# of Values

GxE: using GainRatio * SplitInfo

IxE: using InformationGain * SplitInfo

1/S: using 1/SplitInfo

**-u** file

**-U** file

**-V**

**+v** level or **-v** level

s: work silently

o: show all options set

b: show node/branch count and branching factor

f: show calculated feature weights (default)

p: show value difference matrices

e: show exact matches

as: show advanced statistics (memory consuming)

cm: show confusion matrix (implies +vas)

cs: show per‐class statistics (implies +vas)

cf: add confidence to output file (needs -G)

di: add distance to output file

db: add distribution of best matched to output file

md: add matching depth to output file.

k: add a summary for all k neigbors to output file (sets -x)

n: add nearest neigbors to output file (sets -x)

You may combine levels using '+' e.g. +v p+db or -v o+di

**-w** n

0 or nw: no weighting

1 or gr: weigh using gain ratio (default)

2 or ig: weigh using information gain

3 or x2: weigh using the chi‐square statistic

4 or sv: weigh using the shared variance statistic

5 or sd: weigh using standard deviation. (all features must be numeric)

**-w** file

**-w** file:n

**-W** file

**+%** or **-%**

**+x** or **-x**

(IB1 and IB2 only, default is -x)

**-X** file

# BUGS¶

possibly

# AUTHORS¶

Ko van der Sloot Timbl@uvt.nl

Antal van den Bosch Timbl@uvt.nl

# SEE ALSO¶

2017 November 9 |