The OneR Classifier .. What it is and How it Works

00:06:38
https://www.youtube.com/watch?v=phnkMGDZUNI

Ringkasan

TLDRThe video explains the 1R classifier, which simplifies classification by utilizing a single predictor to generate a rule, improving interpretability. Unlike the Zer R classifier, which ignores predictors, the 1R classifier constructs frequency tables from each predictor against a class variable, selecting the most accurate one using total error. The presenter illustrates this with an example using weather data, showcasing how to build confusion matrices to calculate accuracy. The key takeaway is that while 1R may have lower accuracy than advanced algorithms, it offers a notable understanding of predictors' contributions to classification, making it useful for analysis.

Takeaways

  • 📊 1R uses one predictor for classification
  • 📝 Generates rules based on frequency tables
  • 🌦️ Example used: weather data
  • 📉 Total error measures predictor contribution
  • 🔍 Confusion matrix helps calculate accuracy
  • 📈 71% accuracy shown in the example
  • 🛠️ Simple rules are easy to interpret
  • ⚖️ Numerical predictors need categorization
  • 🔄 Compares predicted vs actual values
  • 📉 No score or probability provided by 1R

Garis waktu

  • 00:00:00 - 00:06:38

    In this segment, the 1R classifier is introduced as a simple yet effective classification algorithm that builds upon the concept of the frequency table used in the Zer R classifier. Unlike Zer R, which ignores predictors, the 1R classifier examines one predictor at a time to generate a classification rule. For each predictor, it creates a frequency table relating the predictor's values to the target class and computes the total error for the rule it generates. The predictor with the smallest total error becomes the chosen rule for classification, making the model easy to interpret. An example using weather data illustrates how frequency tables are constructed, and how the classifier selects the predictor with the highest predictive power, showcasing the simple rules derived from each feature to make predictions about the class variable. The discussion also touches upon the generation of a confusion matrix for further evaluation of the model's accuracy.

Peta Pikiran

Video Tanya Jawab

  • What does 1R stand for?

    1R stands for 'One Rule,' indicating that it generates one rule for classification based on a single predictor.

  • How does 1R work?

    It selects the predictor with the smallest total error after creating frequency tables for classification.

  • Can 1R handle numerical predictors?

    Yes, numerical predictors must be transformed into categorical variables before building frequency tables.

  • What type of outputs does 1R provide?

    1R generates simple classification rules, but does not provide scores or probabilities.

  • What is the accuracy of 1R typically like?

    1R usually produces rules with accuracy slightly less than state-of-the-art algorithms, demonstrated with an example accuracy of 71%.

  • What is the significance of the confusion matrix in this context?

    A confusion matrix is used to measure accuracy by comparing predicted and actual values.

  • What is the main advantage of using 1R?

    It produces simple and interpretable rules for classification.

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Gulir Otomatis:
  • 00:00:01
    welcome back in the last video we
  • 00:00:03
    explained the Zer R classifier and we
  • 00:00:06
    mentioned that in our data it ignores
  • 00:00:09
    the predictors or the features and it
  • 00:00:11
    only builds a frequency table from the
  • 00:00:14
    class variable or from the class column
  • 00:00:17
    and it predicts everything to be the
  • 00:00:20
    same as the majority class so only
  • 00:00:21
    chooses the majority
  • 00:00:23
    class uh the 1R classifier is another
  • 00:00:27
    classifier based on the frequency table
  • 00:00:30
    and the name says it
  • 00:00:33
    all one are short for one rule so what
  • 00:00:37
    it does here instead of ignoring all the
  • 00:00:40
    predictors it only chooses one predictor
  • 00:00:43
    and it uses it for classification it's a
  • 00:00:46
    simple yet accurate classification
  • 00:00:48
    algorithms algorithm and the way it
  • 00:00:51
    works is as follows it generates one
  • 00:00:55
    rule for each predictor in the data then
  • 00:00:58
    selects the rule with with the smallest
  • 00:01:01
    total error as its one rule so from our
  • 00:01:05
    variables or from our predictors or from
  • 00:01:06
    our features we generate one rule for
  • 00:01:09
    each of them and we choose the one that
  • 00:01:11
    gives us the smallest total error to
  • 00:01:14
    create a rule for a predictor we
  • 00:01:16
    construct a frequency table from each
  • 00:01:19
    predictor against the Target now if that
  • 00:01:22
    predictor if that feature is numerical
  • 00:01:25
    then we need to transform it into
  • 00:01:26
    categorical and then build a frequency
  • 00:01:29
    table
  • 00:01:30
    it has been shown that one R produces
  • 00:01:32
    rules only slightly less accurate than
  • 00:01:35
    stateoftheart classification algorithms
  • 00:01:38
    while producing rules that are simple
  • 00:01:39
    for humans to interpret so usually rules
  • 00:01:42
    are quite simple because it only uses
  • 00:01:43
    one predictor or one feature the
  • 00:01:46
    algorithm is as follows for each predict
  • 00:01:49
    predictor for each value of that
  • 00:01:51
    predictor and remember if it's numerical
  • 00:01:53
    then we need to transform it into
  • 00:01:56
    categorical make a rule as follows count
  • 00:01:59
    how often each value of the target class
  • 00:02:02
    appears find the most frequent class
  • 00:02:05
    make the rule assign that class to this
  • 00:02:08
    value of the predictor calculate the
  • 00:02:10
    total error of the rules of each
  • 00:02:12
    predictor and then choose the predictor
  • 00:02:14
    with the smallest total error for that
  • 00:02:17
    to make sense let's take an example
  • 00:02:19
    let's have a look at this this data
  • 00:02:21
    we've seen this before the weather data
  • 00:02:23
    now four predictors or four variables or
  • 00:02:26
    four attributes and one class and the
  • 00:02:28
    Ral categorical so things are nice and
  • 00:02:31
    easy and from this now we build
  • 00:02:33
    frequency tables for each of these
  • 00:02:36
    columns for each of these features
  • 00:02:38
    against the class and as you can see
  • 00:02:41
    here we build our frequency tables and
  • 00:02:44
    from the frequency tables as you can see
  • 00:02:45
    now for Outlook we have three categories
  • 00:02:48
    Sunny overcast rainy and for sunny we
  • 00:02:51
    have three Aces and two nose for
  • 00:02:53
    overcast we have four yeses and zero NOS
  • 00:02:55
    for rainy we have two yeses and three
  • 00:02:58
    NOS for humidity we have high and normal
  • 00:03:00
    for high we have 3 S's and four no for
  • 00:03:03
    normal we have six S's and one no as you
  • 00:03:05
    can as as I mentioned before we uh we
  • 00:03:08
    build the frequency table for each
  • 00:03:11
    attribute for each predictor against the
  • 00:03:14
    class and by the way the sum of all of
  • 00:03:17
    these should be the same as the number
  • 00:03:19
    of instances these should be all of our
  • 00:03:21
    instances if for example here we have 14
  • 00:03:24
    instances if these numbers don't don't
  • 00:03:27
    sum to 14 then something is not right
  • 00:03:32
    now from this we can now build uh uh um
  • 00:03:36
    um maybe we can build um a confusion
  • 00:03:40
    Matrix to calculate the accuracy or
  • 00:03:45
    somehow find the way of uh uh measuring
  • 00:03:47
    the error and we notice now that if we
  • 00:03:51
    build a confusion Matrix out of this we
  • 00:03:53
    notice that the outlooks the Outlook
  • 00:03:56
    gives us the highest accuracy
  • 00:04:01
    um and as you can see here our rules are
  • 00:04:04
    nice and easy if out our one R now if
  • 00:04:08
    Outlook is sunny then play golf equals
  • 00:04:11
    yes and the reason is uh because for
  • 00:04:15
    sunny the vast majority is yeses three
  • 00:04:18
    is larger than no if Outlook is overcast
  • 00:04:22
    then play golf equals yes and as you can
  • 00:04:24
    see now we don't have any nose for out
  • 00:04:26
    for overcast so that's quite easy and
  • 00:04:28
    for Outlook rainy play golf equals no
  • 00:04:31
    because the number of NOS is larger than
  • 00:04:32
    the number of uh yeses this is
  • 00:04:36
    explaining what we mentioned
  • 00:04:39
    here find the most frequent class so as
  • 00:04:43
    you can see here
  • 00:04:45
    um for example for sunny we have three
  • 00:04:48
    Aces and two NOS then if Outlook is
  • 00:04:50
    sunny then we choose yes always yes if
  • 00:04:53
    Outlook is overcast we have zero and
  • 00:04:55
    four and zero so we always choose four
  • 00:04:57
    and for rain you have two and three so
  • 00:04:59
    always choose three IE no yes now the
  • 00:05:03
    contribution of the predictors in this
  • 00:05:05
    classifier simply the total error
  • 00:05:07
    calculated from the frequency tables is
  • 00:05:10
    the measure of each predicted
  • 00:05:13
    contribution a low total error means a
  • 00:05:16
    higher contribution to the pr
  • 00:05:18
    predictability of the
  • 00:05:22
    model just to show you we can actually
  • 00:05:24
    show a confusion Matrix for example for
  • 00:05:28
    uh um for Outlook
  • 00:05:30
    look and we can build it as we learned
  • 00:05:35
    before actual values yeses and NOS
  • 00:05:38
    predicted values yes and Nos and we see
  • 00:05:41
    how many true positives how many true
  • 00:05:43
    negatives how many false positives how
  • 00:05:46
    many false negatives and compute the
  • 00:05:47
    accuracy we can do this maybe for uh um
  • 00:05:53
    each of these attributes and choose the
  • 00:05:55
    one with the highest accuracy in our
  • 00:05:57
    case it will be
  • 00:06:02
    Outlook um it does actually show by the
  • 00:06:05
    way a significant predictability power
  • 00:06:08
    and accuracy of 71% isn't that bad is it
  • 00:06:12
    one R does not generate score or
  • 00:06:15
    probability that's why we uh we don't
  • 00:06:18
    have uh gains or lift charts or KS
  • 00:06:21
    charts or Rock charts the one we the
  • 00:06:23
    ones we explained in our model
  • 00:06:25
    evaluation tutorial I'm going to stop
  • 00:06:27
    here uh in the next video video I will
  • 00:06:30
    start explaining the basic ideas and the
  • 00:06:33
    intuition behind the naive Bas
  • 00:06:35
    classifier thanks again and I'll see you
  • 00:06:37
    next time
Tags
  • 1R Classifier
  • Classification
  • Frequency Table
  • Predictors
  • Weather Data
  • Confusion Matrix
  • Accuracy
  • Simple Rules
  • Predictive Power
  • Machine Learning