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Z-scores

              A statistical measurement known as the Z-score indicates how closely a value relates to the mean of a group of values.
              The Z-score is calculated using standard deviations from the mean. When a data point's Z-score is 0, it means that it has
              the same score as the mean. One standard deviation from the mean would be indicated by a Z-score of 1.0. Z-scores can
              be positive or negative; a positive value means the score is above the mean, while a negative value means it is below the
              mean. Z-score can be calculated using the following formula:
              z = ( x - μ ) / σ
              where:
              •  z = Z-score
              •  x = the value being evaluated
              •  μ = the mean
              •  σ = the standard deviation
              Example:
                  x = 59
                  μ = 52
                  σ = 4
              Applying the formula, z = (59 - 52 ) / 4, z = 1.75
              So, your selected value has a z-score that indicates it is 1.75 standard deviations from the mean.



                        At a Glance


                    •  Statistical methods are required to understand the data used to train a machine learning model and to
                     interpret the results of testing different machine learning models.
                    •  Structured data is formatted, and has a predefined data type and arrangement.
                    •  A variable is a characteristic that is measured and can have multiple values. In other words, something that
                     varies from case to case.
                    •  The level of measurement refers to the relationship between the values given to the attributes of a variable.
                    •  In nominal measurement, the numerical values represent a unique “name” of the attribute.
                    •  In ordinal measurement, attributes can be ordered. The distances or intervals between attributes are irrelevant
                     here.
                    •  While measuring intervals, the distance between attributes is important.
                    •  Ratio measurements are based on proportion and can have an absolute zero value.
                    •  The tabular format to present cases and variables used in statistical study is known as a data matrix.
                    •  The frequency of a particular data value is the number of occurrences of that data value in a dataset.
                    •  The shape of data distribution in a graph is described by its number of peaks and their symmetry, their
                     tendency to skew, or their uniformity.
                    •  A graph can be unimodal, bimodal or multimodal.
                    •  Distribution is symmetric if its left half forms a mirror image of its right half.
                    •  A distribution that is not symmetric has values that tend to be more spread out on one side of the graph
                     than on the other.
                    •  A distribution that is not symmetric can be left-skewed or right-skewed.
                    •  In Symmetric distribution, the median, mean, and mode are the same.
                    •  The mean is the average or the most common value in a group of numbers.
                    •  The median is the middle value in a given list of numbers arranged in ascending or descending order.
                    •  The mode is the value that appears most often in a given list of numbers.



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