Machine Learning
  • Introduction
  • Self LEarning
  • Why Statstics in ML Or Data Science
  • How important is interpretability for a model in Machine Learning?
  • What are the most important machine learning techniques to master at this time?
  • Learning
    • Supervised Learning
      • Evaluating supervised learning
        • K-fold cross validation
        • Using train/test to prevent overfitting of a polynomial regression
      • Regression
        • Linear regression
          • The ordinary least squares technique
          • The gradient descent technique
          • The co-efficient of determination or r-squared
            • Computing r-squared
            • Interpreting r-squared
          • Assumptions of linear regression
          • Steps applied in linear regression modeling
          • Evaluation Metrics Linear Regression
          • p-value
        • Ridge regression
        • Least absolute shrinkage and selection operator (lasso) Regression
        • Polynomial regression
        • Performance Metrics
        • Regularization parameters in linear regression and ridge/lasso regression
        • Comments
      • Classification
        • test
        • Logistic Regression
        • naïve Bayes
        • support vector machines (SVM)
        • decision trees
          • Split Candidates
          • Stopping conditions
          • Parameters
            • Non Tunable Or Specificable
            • Tunable
            • Stopping Parameters
        • Evaluation Metrics
      • Random Forest
        • Logistic Regression Versus Random Forest
        • Paramters
          • Non Tunable Parameters
          • Tunable
          • Stopping Param
        • Parameter Comparison of Decision Trees and Random Forests
        • Classification and Regression Trees (CART)
        • How random forest works
        • Terminologies related to random forest algorithm
        • Out-of-Bag Error
      • Decision Trees
        • Gini Index
    • Unsupervised learning
      • Clustering
        • test
        • KMeans Clustering
          • Params
          • Functions
        • Gaussian Mixture
          • Parameters
          • functions
    • Semi-supervised learning
    • Reinforcement learning
    • Learning Means What
    • Goal
    • evaluation metrics
      • Regression
        • MSE And Root Mean Squared Error (RMSE)
        • Mean Absolute Error (MAE)
      • Model Validation
        • test
      • The bias, variance, and regularization properties
        • Regularization
          • Ridge regression
        • Bias And Variance
      • The key metrics to focus
    • hyperparameters
  • Steps in machine learning model development and deployment
  • Statistical fundamentals and terminology
  • Statistics
    • Measuring Central Tendency
    • Probability
    • Standard Deviation , Variance
    • root mean squared error (RMSE)
    • mean Absolute Error
    • explained Variance
    • Coefficient of determination R2
    • Standard Error
    • Random Variable
      • Discrete
      • Continuous
    • Sample vs Population
    • Normal Distribution
    • Z Score
    • Percentile
    • Skewness and Kurtosis
    • Co-variance vs Correlation
    • Confusion matrix
    • References
    • Types of data
      • Numerical data
        • Discrete data
        • Continuous data
      • Categorical data
      • Ordinal data
    • Bias versus variance trade-off
  • Spark MLib
    • Data Types
      • Vector
      • LabeledPoint
      • Rating
      • Matrices
        • Local Matrix
        • Distributed matrix
          • RowMatrix
          • IndexedRowMatrix
          • CoordinateMatrix
          • BlockMatrix
    • Comparing algorithms supported by MLlib
      • Classification
    • When and why should you use MLlib (versus scikit-learn versus TensorFlow versus foo package)
    • Pipeline
    • References
    • Linear algebra in Spark
  • Terminology
  • Machine Learning Steps
    • test
  • Preprocessing and Feature selection techniues
  • The importance of variables feature selection/attribute selection
    • Feature Selection
      • forward selection
      • mixed selection or bidirectional elimination
      • backward selection or backward elimination
      • The key metrics to focus on
  • Feature engineering
  • Hyperplanes
  • cross-validation
  • Machine learning losses
  • When to stop tuning machine learning models
  • Train, validation, and test data
  • input data structure
  • Why are matrices/vectors used in machine learning/data analysis?
    • Linear Algebra
  • OverView
  • Data scaling and normalization
  • Questions
  • Which machine learning algorithm should I use?
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  • Mean
  • Median
  • What is wrong with the mean and median?
  • Mode

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  1. Statistics

Measuring Central Tendency

PreviousStatisticsNextProbability

Last updated 5 years ago

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The Statsville Health Club prides itself on its ability to find the perfect class for everyone. Whether you want to learn how to swim, practice martial arts, or get your body into shape, they have just the right class for you. The staff at the health club have noticed that their customers seem happiest when they’re in a class with people their own age, and happy customers always come back for more. It seems that the key to success for the health club is to work out what a typical age is for each of their classes, and one way of doing this is to

calculate the average

. The average gives a representative age for each class, which the health club can use to help their customers pick the right class.

Mean

Then mean is

Mean is represented with u ( MEW)

Sometimes mean will be pulled or impacted by outliers - An extreme high or low value that stands out from the rest of the data. When outliers “pull” the data to the left or right, it is called Skewed Data.

Median

If the mean becomes misleading because of skewed data and outliers, then we need some other way of saying what a typical value is. We can do this by, quite literally, taking the middle value. This is a different sort of average, and it’s called the median

. To find the median of the Kung Fu class, line up all the ages in ascending order, and then pick the middle value

If you have an even set of numbers, just take the mean of the two middle numbers

(add them together, and divide by 2), and that’s your median.

What is wrong with the mean and median?

The mean and median for the class are both 17, even though there are no 17-year-olds in the class!

But what if there had been an odd number of people in the class. Both the mean and median would still have been misleading.

Additional Problem is

Why do you think the mean and median both failed for this data? Why are they misleading?

Both the mean and median are misleading for this set of data because neither fully represents the typical ages of people in the class. The mean suggests that teenagers go to the class, when in fact there are none. The median also has this problem, but it can fluctuate wildly if other people join the class.

If you had to pick one age to represent this class, what would it be? Why?

It’s not really possible to pick a single age that fully represents the ages in the class. The class is really made up of two sets of ages, those of the children and those of the parents. You can’t really represent both of these groups with a single number.

Mode

The mode of a set of data is the most popular value, the value with the highest frequency. Unlike the mean and median, the mode absolutely has to be a value in the data set, and it’s the most frequent value.

If a set of data has two modes, then we call the data bimodal

. The mode doesn’t just work with numeric data; it works with categorical data, too. When you’re dealing with categorical data, the mode is the most frequently occurring category.

When do you think the mode is most useful?

  • When the data set has a low number of modes, or when the data is categorical instead of numerical. Neither the mean nor the median can be used with categorical data.

When is the mode least useful?

  • When there are many modes

Which is to use When