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Loss Functions | MSE | MAE | RMSE

            Performance Metrics The various metrics used to evaluate the results of the prediction are : Mean Squared Error(MSE) Mean Absolute error(MAE) Root-Mean-Squared-Error(RMSE) Adjusted R² Mean Squared Error: Mean Squared error is one of the most used metrics for regression tasks. MSE is simply the average of the squared difference between the target value and value predicted by the regression model.  As it squares the differences and  penalizes (punish)even a small error which leads to over-estimation of how bad the model is. It is preferred more than other metrics because it is differentiable and hence can be optimized better. in the above formulae, y=actual value and ( yhat) means predicted value by the model. RMSE(Root Mean Squared Error: This is the same as MSE (Mean Squared Error) but the root of the value is considered while determining the accuracy of the model. It is preferred more in some cases because the errors are first...
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WHAT IS BOOSTING

                                                     BOOSTING What is Boosting? Boosting and Bagging algorithms are belonging to the ensemble learning techniques. Boosting refers to converting weak learners into Strong learners. what are weak learners? if our base model predicts wrongly(like spam mail as not spam) the features are called weak learners. weak learners are nothing but the  To convert Weak learners into strong learners we generally apply weights. Let's take an example. Note:-The base learner may be any model like decision Tree, SVM, etc... we have a dataset with 100 features, in the ensemble technique we have base learners here what we do is we take some amount of data in the first base learner. in our first base learner model, the model predicts some features are not predicted well These w...
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LSTM

                                                                                  LSTM  we will discuss now each and every stage with the help of the above diagram. State1:Memory View The memory view is responsible for remembering and forget the information based on the context of an input. (you didn't get it, wait now you will understand). In the above diagram, the memory view is the top line.   key points( Ct-1, X, +,  and Ct) . The input is an old memory, X is multiplication which forgets the useless information from the old memory, and " +"   addition lets merge all these things. when we multiply the old memory with '0' the old memory will "0" or if we multiply with vector "1" The old memory won't change. ( what ...
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SUPPORT VECTOR MACHINE

                 SUPPORT VECTOR MACHINE:- Support vector machine:-it is a type of supervised learning algorithm it is used to solve both classification and regression problem. Note :- It is mostly used for classification problems. what we are going to learn in SVM: a) Support vectors b) Hyperplane c) Marginal Distance d) Linear Separable e) Non-linear separable f) support kernels NOw we will discuss everything in detail. Hyper plane:- in the above diagram, we have drawn three lines(A, B, C) separating two data points (stars and reds) The lines (A, B, C) are called Hyperplanes. Note:- “Select the hyper-plane which segregates the two classes better” i.e  above there are three hyperplanes how to select the best hyperplane? b)Marginal Distance:- When we draw a hyperplane the plane creates two new(------) dotted lines one line above the hyperplane and one line below the hyperplane line. see the below image you will get an ...
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KNN Interview Questions

                           KNN interview questions 1) Which of the following distance metric can not be used in k-NN? A) Euclidean Distance B) Manhatten Distance c) Hamming Distance E) Minkowski Distance F) Jaccard Distance G) All the above Answer:- G All of these distance metric can be used as a distance metric for KNN 2)Knn is for regression or classification? Answer:- Knn is used for both classification and regression problems. 3) When we use Manhatten Distance? Answer:-Manhatten distance is used for continuous variables. 4) You have given the following 2 statements, find which of these options is/are true in case of k-NN? In the case of very large value of k , we may include points from other classes into the neighborhood, so it leads to overfitting. In case of too small value of k the algorithm is very sensitive to noise.(it will affect our model performance). Answer:-The above two points are answers. 5...
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🌲🌳 Decision Tree 🌲🌳

                               🌲🌳 Decision Tree 🌲🌳 Decision Tree   Algorithm has come under supervised Learning, it is used for both  Regression and Classification. Important Terminology related to Decision tree: Root Node:  It represents the entire population or sample and this further gets divided into two or more homogeneous sets. Splitting:  It is a process of dividing a node into two or more sub-nodes. Decision Node:  When a sub-node splits into further sub-nodes, then it is called the decision node. Leaf / Terminal Node:  Nodes do not split is called Leaf or Terminal node. Pruning:  When we remove sub-nodes of a decision node, this process is called pruning. You can say the opposite process of splitting. Branch / Sub-Tree:  A subsection of the entire tree is called a branch or sub-tree. Parent and Child Node:  A node, which ...
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K-NN

                           K-Nearest Neighbour The k-nearest neighbors (KNN) algorithm is a simple, easy-to-implement supervised machine learning algorithm that can be used to solve both classification and regression problems. KNN means in short Similar things near to each other. The KNN algorithm uses ‘ feature similarity ’ to predict the values of any new data points. I am going to explain this knn with a simple example:- In the above table, we have S.No, Height, Weight & Age in our table for S.No.5 the weight is missing, So now we need to predict the weight of the person based on his Height and Age. graph example in the above graph, X_axis represents the age and the y_axis represents the Height of a person. in the above graph, I write 5 numbers, in that  4 values have output and one id not having output now see How KNN help us. the 5th number I want to predict which is circled. hint:-By seeing th...
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