Random Forest

-

 











































Comments

Post a Comment

Popular posts from this blog

WHY DEEP NEURAL NETWORK: Difference between neural network and Deep neural network

-
Image
  WHY DEEP NEURAL NETWORK : Difference between neural network and Deep neural network .  Labeled data set is the prime requirement of Supervised learning . As a .  massive data is generated every day, AI Projects are innovated with rapid speed and choice of the algorithm is a critical issue: Choice of the algorithm depends  1.       Number of data sets (samples) and attributes  in that data set.(Dimensions of input and                 output). 2 .      Linearity and non-linearity in that data set 3   Performance. Neural Network or Deep Learning:  Algorithmic steps of neural network are   1.    Need to decide Architecture of NN i.e.Number of hidden layers and number of units in that hidden layer. 2.     Apply training algorithm by Deciding hyperparameters like, Transfer function, No of iteration, learning rate etc to train the network. 3.     Test the performance   Major disadvantages are Ø   If required performance is not achieved we need to repeat step 1 to 3 again.This
Read more

Training and Testing Phase of Neural Network

-
Image
Training and Testing Phase of Neural Network: Training and Testing is an important part of Artificial Neural Network.Inborn baby's biological Neural Network is untrained and unable to recognize  environment.Parents slowly started to train the baby and baby will recognize the things.  Every neural network  it may be Natural or Artificial need to train the network and then test the network.If network is unable to do the task retrain with additional examples.Natural and Artificial neural network need to train . For every Neural Network following are the major steps to build the algorithm: 1. Collection of data          2. Training           3. Testing             4. Validation 5. Performance function
Read more

SVM Algorithm for Python Code

-
  For every Machine Learning Algorithm following are the major steps to build the Model: 1. Collection of data          2. Divide data into training and testing           3. Build the Network 5. Performance Analysis and Accuracy calculations Step1 : Load dataset #Import scikit-learn dataset library from sklearn import datasets  Ex. cancer = datasets.load_breast_cancer()   Step 2:Split dataset into a training set and test set Import train_test_split function from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(cancer.data, cancer.target, test_size=0.3,random_state=109) # 70% training and 30% test Step3:Create a svm Classifier   Import svm model from sklearn import svm clf = svm.SVC(kernel='linear') # Linear Kernel clf = svm.SVC(kernel='linear') # Linear Kernel   Step4:Train the model using the training sets clf.fit(X_train, y_train)   Step5:Predict the response for test dataset y_pred = clf.predict(X_t
Read more