Kim 2 ML

Starting off as a muggle that naïve to the Math's and Data Science world.

Blog

  • Sell me this pen

    Sell me this pen

    Link: Instagram Round 1 Customer: Ok, i wanna try something, sell me this pen.Salesman: Sir, this is a marker. (Created a tailwind) Round 2 Customer: Okay, sell me that marker.Salesman: Do you need a marker? (Checking for a demand)Customer: Well, i actually don’t need a marker right now. Round 3 Salesman: Then why do you…

  • Predicting 100% in IRIS dataset

    Predicting 100% in IRIS dataset

    dataset: https://archive.ics.uci.edu/dataset/53/iris While scrolling through YouTube, I came across this video:https://www.youtube.com/watch?v=MdOCu2Gr-0g It explores Fibonacci numbers, which sparked a thought—could I experiment with them in a unique way, perhaps using the Iris dataset? First, let’s create a sequence of Fibonacci numbers. Result: Next, we create a function that returns the largest Fibonacci number closest to a…

  • GDG DevFest KL 2024

    When: Sat 2024-12-07 9am – 6pm (MYT)Where: Level 2, Menara Ken TTDI, 37, Jalan Burhanuddin Helmi, Taman Tun Dr Ismail, 60000 Kuala Lumpur, Wilayah Persekutuan Kuala Lumpur Malaysia

  • AI Tinkerers Kuala Lumpur #6

    When: Mon 2024-11-28 6pm – 9pm (MYT)Where: Level 8, Menara Southpoint @ Mid Valley City, Medan Syed Putra Selatan, 59200 Kuala Lumpur , Malaysia Continue to learn the way of communication WRONG EXAMPLE Kim: Hi, I’m Kim Chye from Western Digital. [I did not ask how are you, or saying great speech]B: Hey, so what…

  • Quiz

    Quiz

    Saw a challenge in LinkedIn about creating a tree and here is my try =P resolution from the internet.

  • Multilevel Data Analysis

    Day 94 Multilevel AnalysisContextual AnalysisMixed Effects Models Day 95 Tutorial Day 96 Data collected wronglyType of mistake occur Day 97 Variable Proposition Day 98, Day 98 (2) TutorialStatistical Treatment of Clustered DataMethod– Aggregation– Disaggregation Day 99, Day 99 (2), 101 (2), 103 DisaggregationHow to avoid? Using two-stage sample.Looking for correlationCorrelation Analysis (Intraclass Correlation-ICC)F-testLinear RegressionTotal Regression, Regression…

  • Day 106

    Question 1 As a variable at the student level that is essential for explaining Economics score, we use the measure for revision hours per month taken from a study. The revision hours has been centered, so that its mean is 0. The results are presented below. Revision hours here is the variable with overall centering…

  • Day 105 (2)

    Hypothesis Test t-test = sample size < 30, t distributionz-test = sample size > 30, z distribution For multi-level regression, t-test is different as needed to handle 2 level. ExampleModel 1 = grand-mean-centered IQ variable along with the group mean Model 2 = with-in group deviation variable Only Y01 is different between model Between group…

  • Day 105

    Example Regression Line given by Yij = 41.13 + 2.4801 IQij + 1.029 IQ (meanj) + Uoj + U1j IQij + RijStandard Deviation of random slope “Language Score” = √0.195 = 0.44Average slope = 2.480 Correlation between random slope and random intercept = -0.83/√(8.88×0.195) = -0.63The negative correlation means that classes with a higher performance…

  • Day 104

    Question 1 Compare the Multiple Linear Regression (MLR) and Random Intercept Model (RIM). Example Answer MLR RIM Question 2 Provide two conditions where random intercept model should be used. Example Answer Question 3 As a variable at the student level that is essential for explaining English score, we use the measure for IQ taken from…

  • Day 102

    Hierarchical Linear Model Advance from Random Intercept Model (simple case of hierarchical linear model) by introducing random slopes. RIM handle group difference with respect to average value of independent variable. Yij = β0j + β1j Xij + Rij become… Yij = γ00 + U0j + γ10 xij + U1j xij + Rij , note this…

  • Day 101

    Example Number of students is M = 3758Number of schools is N = 211Standard Deviation = 2.04 Using Y (Language Score) group by (School) Intraclass Correlation Coefficient (ρI) = 18.12/(18.12+62.85) = 0.22Standard Deviation of “Language Score” = √(18.12+62.85) = 9.00 Scale for effect that is independent of the measurement units = 2.04/9.0 = 0.57Each additional…

  • Day 100

    Random Intercept Model aka Ordinary Least Square (OLS) model Some brief introduction, before start. Fixed Effect (Taken only 1 group, level 1) Exception, when all group sample size nj are equal to one. The researcher need to have no qualied about using the model because nesting structure are not take place in this situation. The…

  • Day 99 (2), 101 (2), 103

    Question 1 Discuss the disadvantages of “aggregation” Example Answer When we are using aggregation, the detail about the data point or micro data is ignored in order to conclude the overall picture; which is the macro level. By reducing each aspect or characteristic, it may lead to misleading conclusion. For example, determine the performance of…

  • Day 99

    Disaggregation Disaggregation lead to “miraculous multiplication of the number of units” Suppose we want to understand “do new judges tend to give more harsh or more lenient sentences”? However taking observation base on (44) trials instead of (6) judges to estimate the relation is wrong. Sample size is exaggerated. From the perspective of between group…