Describing the use of Statistics
* Its important to skim through some of the basic statistical concepts related to probability and statistics . Along with that , we will also try to understand how these concepts can help someone to describe the information used by machine learning algorithms
* The zist of all the learning of these concepts is not only
about how to describe an event by counting the number of occurrences , but its
about describing an event without counting every time how many times a
particular event occurs .
* If there are some imprecision in a recording instrument that one uses , or simply because some error in the the recording procedure of a machine occurs , rather an imprecision occurs in the instrument that one uses or simply because of any random nuisance which disturbs the process of recording a given measure during the process of recording the measure occurs ... then a simple measure such as weight , will differ every time one would get a scale which would be slightly oscillating around the true weights and minimal variation scale . Now , if someone wants to perform such a small incident in a city and want to measure the weight of all the people in the city , then it is probably an impossible experiment to be conducted on such a large scale as it would involve taking the weight-wise reading of all the people in the city which is something that is practically not possible , because first of all if someone wants to perform this experiment in one go , then one has to create a big big gigantic weighing scale to mount all the people of the city in its weighing pans , which is completely an impossible task , and probably the scale may break once all the people have been mounted to the pan or otherwise the worst thing that may happen is that once all the people's weights have been measured , the experiment could render itself insignificant as the experiment once conducted would make the use of the weighing machine useless and hence the cost associated with building of such a big machine for carrying out just one task would become meaningless .
* So , the purpose of experiment might get achieved , but the cost of the built-up of the instrument would run so high that a big dent in the overall GDP of the city would get created which might cripple the city's finance and budget . On the other hand , if we take the measurement of the entire city's weight recording each person's weight one by one , then the effort and time taken for the entire activity to be completed might take some weeks or months of time . Because of the high amount of time and effort that would get consumed while managing the entire ruckus won’t suit the idea for adaptability and taking up the idea. And even if all the weights of the people residing in the city is successfully measured, there are a lot of chances that anyhow some amount for error would definitely popup making the idea of the entire process not so fruitful and fault-proof
* Having partial information is a quite complex process which is not a completely negative condition because one can use such smaller matrices for the purpose of efficient and less cumbersome calculations. Also on top of that, it is said that one cannot get a sample of what one wants to describe and learn because the event's complexity might be quite high and may probably feature a great variety of features . Another example that some users could consider while taking a case of a sample or a large case of data , is a case of Twitter tweets . All the tweets may be considered as some sample of data over where the same data could be treated as some experimental potions and minerals which are processed using several word processors , sentiment analyzers , business enhancers , spam , abusive data and all depending upon the sample of data associated with each of the text within the short frame of data that one can provide within the text section .
* Therefore it is also a good practice in sampling to sample similar data which has associated characteristics and features which will present the sample data in the form of a grouped cohesive data which fit into a proper sampling criteria. And when sampling is done carefully, one can get an idea that one can obtain a better global view of the data from the constituent samples
* In Statistics , a population refers to all the events and objects that one wants to measure and is a part of the given criteria which gives in detail the account of metrices of the population . Using the concept of random sampling , which is picking the events or objects one needs to choose one's examples according to the criteria which would determine how the data is collected ,assembled and synthesised. This is then used for feeding into machine learning algorithms which apply their inherent functions for determination of patterns and behaviour.
* Along with such determination , a probabilistic model of input data is built which is used for prediction of similar patterns from any newly input data or datasets ,Application of this concept of data generation from population's subsamples and mapping the identified patterns to map new use cases is one of subsamples the chief objectives of machine learning on the back of supported algorithms
* "Random Sampling" -- It is not the only approach for any sort of sampling . One can also apply an approach of "stratified sampling" through which one can control some aspects of the random sample in order to avoid picking too many or too few events of a certain kind .After all , it is said that a random sample is a random sample , the manner it gets picked is irrespective of the manner in which all samples would criterion themselves for picking up a sample , and there is no absolute assurance of always replicating an exact distribution of a population .
* A distribution is a statistical formulation which describes how to observe any event or a measure by ideating the probability of witnessing a certain value . Distributions are described in Mathematical formula and can be graphically described using charts such as histograms or distribution plots . The information that one wants to put over the matrix has a distribution , and one may find that the distributions of different features are related to each other . A normal distribution naturally implies variation and when dealing with numeric values , it is very important to figure out a center of variation which is essentially a value which corresponds to the statistical mean which can be calculated by summing all the values and then dividing the sum by the total number of values considered .
* Mean - This is specifically a descriptive measure which tells the users the values to expect the most from within dataset . as it is a general fact that most of the times , one can observe that the mean of a dataset is that data which generally hovers around a given data group or the entire dataset . The Mean of a dataset is the best suited data for any symmetrical and bell-shaped distribution . In cases , when the value is above the mean of the entire dataset , the distribution is similarly shaped for the values that lie below the mean . The normal distribution or the Gaussian distribution is shaped around the mean which one can find only when one is dealing with legible data which is not much skewed in any direction from the equally shaped domes of the normal distribution curve . In the real world , in most of the datasets one can find many skewed distributions that have extreme values on one side of the distribution , which influences the value of mean so much
* Median - The Median is a measure that takes the value in the
middle after one orders all the observations from smallest to the largest
values within the dataset . Based on the value order, the median is a less
approximate measure of central approximation of data .
* Variance - The significance of mean and median data descriptors is that they describe a value within a data description around which there is some form of variation . In general, the significance of the mean and median descriptors is variation. In general , the significance of the mean and median descriptors is that they describe a value within the distribution around which there is a variation and machine learning algorithms generally do not care about such a form of variation . Most people generally refer to the term , variation as "variance" . And since , variance is a squared number there is also a root equivalent which is termed as "Standard Deviation" . Machine Learning takes into account the concept of variance in every single variable (univariate distributions) and in all the features together (multivariate distribution) to determine how such a variation impacts the response obtained .
* Statistics is an important matter in machine learning because it conveys the idea that features have a distribution pattern . Distribution of data implies variation and variation means quantification of information ... which means that more amount of variance is present in the features , then the more amount of Information can be matched to the response .
* One can use statistics to assess the quality of the feature
matrix and then leverage statistical measures in order to draw a rule from the
types of information to their purposes that they cater to .
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