Deep Learning

Deep Learning Concepts


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Support Vector Machines

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Support Vector Machines A Support Vector Machine (SVM) is a powerful supervised machine learning model used for classification. An SVM makes classifications by defining a decision boundary and then seeing what side of the boundary an unclassified point falls on. Decision boundaries get defined,  by using a training set of classified points.     Decision boundaries exist even when your data has more than two features. If there are three features, the decision boundary is now a plane rather than a line.   As the number of dimensions grows past 3, it becomes very difficult to visualize these points in space. Nonetheless, SVMs can still find a decision boundary. However, rather than being a separating line, or a separating plane, the decision boundary is called a separating hyperplane . Optimal Decision Boundaries In general, we want our decision boundary to be as far away from training points as possible. Maximizing the dis...
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Random Forest

RANDOM FOREST A random forest is an ensemble machine learning model. It makes a classification by aggregating the classifications of many decision trees. Random forests are used to avoid overfitting. By aggregating the classification of multiple trees, having overfitted trees in a random forest is less impactful. Every decision tree in a random forest is created by using a different subset of data points from the training set. Those data points are chosen at random with replacement , which means a single data point can be chosen more than once. This process is known as bagging . When creating a tree in a random forest, a randomly selected subset of features are considered as candidates for the best splitting feature. If your dataset has n features, it is common practice to randomly select the square root of n features. Boosting Steps : Draw a random subset of training samples d1 without replacement from the training set D to train a weak learner C1 ...
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ACCURACY, RECALL, PRECISION, F1 SCORE

ACCURACY, RECALL, PRECISION, F1 SCORE Classifying a single point can result in a true positive ( truth = 1 , guess = 1 ), a true negative ( truth = 0 , guess = 0 ), a false positive ( truth = 0 , guess = 1 ), or a false negative ( truth = 1 , guess = 0 ). Accuracy measures how many classifications your algorithm got correct out of every classification it made. Recall measures the percentage of the relevant items your classifier was able to successfully find. Precision measures the percentage of items your classifier found that were actually relevant. Precision and recall are tied to each other. As one goes up, the other will go down. F1 score is a combination of precision and recall. F1 score will be low if either precision or recall is low. The decision to use precision, recall, or F1 score ultimately comes down to the context of your classification. Maybe you don’t care if your classifier has a lot of false positives. If that’s the case, precision doesn’t matter as m...
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