Sharat Sachin Maximizing my potential

AWS ML Speciality (Part 2.1)

This post gives a quick review on sanitizing and preparing data for modelling.

Sanitizing and preparing data for modelling

Data labeling tools

SageMaker Ground Truth

  • data labeling service to label data with the option to use human annotators through Amazon Mechanical Turk, third-party vendors, or own private workforce
  • also generate labeled synthetic data without manually collecting or labeling real-world data

SageMaker Ground Truth Plus

  • create high-quality training datasets without building labeling applications or managing workforces
  • provides an expert workforce that is trained on ML tasks
  • upload your data, and then SageMaker Ground Truth Plus creates and manages data labeling workflows and the workforce on your behalf

Mechanical Turk

  • crowdsourcing marketplace
  • makes it easier for customers to outsource jobs to a distributed workforce to perform virtually

Descriptive Statistics

  • aim to summarize a sample (summary statistics)
  • in contrast to inferential statistics - which use the data to learn about the population that the sample of data is thought to represent
  • types:
    • a measure of location, or central tendency
      1. mean
        • susceptible to the influence of outliers and skewed data
      2. median
        • less affected by outliers and skewed data
      3. mode
    • a measure of statistical dispersion
      1. standard deviation
        • appropriate when the continuous data is not significantly skewed or has outliers
        • sample formula
        • population formula
      2. variance
        • square of standard deviation, same problems, different units too
      3. IQR (Interquartile range) \( = Q3 - Q1\)
        • much less affected by outliers or a skewed data set
        • semi-interquartile range \( = \frac{1}{2} (Q3 - Q1)\)
        • do not take into account every data point
    • a measure of the shape of the distribution
      1. skewness
        • measure of the lack of symmetry. A distribution, or data set, is symmetric if it looks the same to the left and right of the center point.
        • negative/left-skewed, left-tailed -> left tail being drawn out
          • often leads to the mean being skewed to the left of median
          • but not a necessity
        • positive/right-skewed, right-tailed -> vice-versa
      2. kurtosis
        • high kurtosis -> heavy tails, or outliers
        • low kurtosis -> light tails, or lack of outliers
    • a measure of statistical dependence
      • Pearson correlation coefficient
        • for evaluating linear relationship between two continuous variables
      • Spearman correlation coefficient
        • nonparametric measure of rank correlation
        • works with monotonic relationships as well
  • list of probability distributions

Formatting, normalizing, augmenting, and scaling data

  • about data cleansing
  • data augmentation - to increase amount of data by adding slightly modified copies of already existing data or newly created synthetic data from existing data
    • computer vision
      • cropping, flipping, translation, scaling, rotation, color, adding noise
    • NLP
      • synonym replacement, text substitution, random insertion/swap/deletion, word/sentence shuffling
    • audio
      • cropping out a portion of data, noise injection, shifting time, speed tuning changing pitch, mixing background noise and masking frequency
  • about feature scaling

Inferential statistics

  • gain understanding of the population data by analyzing the samples obtained from it
  • hypothesis testing:
    • test assumptions and draw conclusions about the population from the available sample data
    • involves setting a null and alternative hypothesis, then conducting a statistical test of significance
    • hypotheses :
      • alternative hypothesis : \(H_1 \rightarrow \) there is an effect
        • the thing we are trying to prove
      • null hypothesis : \(H_0 \rightarrow \) there is no effect
        • opposite of alternative, or the status quo
        • should include equality \( (\leq or \geq or =) \)
      • hypotheses are always about the population parameters, not the sample values / statistics
    • level of significance : \(\alpha = 0.05\)
      • probability you will say \(H_0\) is wrong when it is correct
        • Type 1 error
    • \(p\)-value : probability that if the null hypothesis were true, sampling variation would produce an estimate that is further away from the hypothesised value than our data estimate
      • how likely is it to get a result like this if the null hypothesis were true
      • if \(p < \alpha \rightarrow \) reject the null hypothesis
        • \(p\) is low, null must go
      • if \(p >= \alpha \rightarrow \) unable to reject the null hypothesis
    • z-test
    • t-test

Handling missing values and outliers

  • Article on handling missing data
  • Multiple Imputations by Chained Equations (MICE) algorithm
    • imputes or ‘fills in’ the missing data in a dataset through an iterative series of predictive models
    • each specified variable in the dataset is imputed in each iteration using the other variables in the dataset
    • iterations will be run continuously until convergence has been met
    • MICE is a better imputation method than naive approaches (filling missing values with 0, dropping columns)
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