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Supervised machine learning algorithms : The supervised learning is that you have given data set and all is concoct a relationship between the input and the output. Regression We need a continuous output that should not be in discrete terms. Classification - The output should be in discrete terms that are it should be either yes (1) or no (0).
If we are provided with a data set of the size of the tumor with the help of supervised classification learning we tell if it as a malign tumor.
The algorithm consists of outcome variable which is predicted from a given set of predictors.
We will generate a function that maps inputs to the desired outputs.
The process continues until the model achieves a level of accuracy on the training data. Examples as, Regression, Decision Tree, Random Forest, KNN, etc.
The algorithm can apply what has been learned in the past to new data using labeled examples to predict future events.
It produces a function to make predictions about the output values.
The system can provide a target for any new input after sufficient training and the algorithm can compare it with output.
The algorithms use sample data to train the algorithm from input and output data.
It contains a target which is to be predicted from any given set of predictors.
So by using a set of variables, we can generate a function that maps inputs to desired outputs.
The process stands until a model achieves a level of accuracy on training data.
It is both a supervised learning method and a statistical method for classification.
It is assumed as an underlying probabilistic model that allows us to capture uncertainty about the model in a principled way.
Naïve Bayes will provide a perspective for understanding and evaluating many learning algorithms.
It will calculate probabilities for hypothesis and it is robust to noise in input data.
Data is prep roved by eliminating instance with missing values and second by applying discretization that converts continuous data attribute values into a finite set.
The classification technique is applied to non-processed data such as Naïve Bayes that is classified with compromising accuracy results.
The representation of data as points in space which is separated into categories by a clear gap.
The new data is mapped into the same space and predicted that belong to a category based on which side of the space they belong to.
Support Vector Machine has an accuracy of 59.5% which is low and has been observed that the performance of SVM classifiers gives better results.
The performance of the classifier will increase with the use of a ranker algorithm and SVM outperformed other classifiers used.
It is observed that the performance of the classifier will increase with the use of the ranker algorithm and SVM outperformed other classifiers.
It is a meta-estimator that fits several decision trees on sub-samples of datasets.
It uses an average to improve the accuracy of the model and control over-fitting.
The size is always the same as the original input sample size but the samples are drawn with replacement.
The algorithms supplement the object from an array of input to a tree of forest and elements of the unit vector.
The forest filters are most voted classification out of the forest and the results express Random Forest Classifier.
The author will suggest the work that can be carried out on combination of hybrid classification algorithms.
The classifier is given a set of data with a class label which produces a set of rules used to classify the data.
The author performs an analysis of the performance of the classifiers using the Synthetic Minority Oversampling Technique.
The Performance of the algorithm is determined and the comparison is made which is based on the accuracy.