What ensemble technique is used by Random forests?
Bagging is the technique used by Random Forests. Random forests are a collection of trees which work on sampled data from the original dataset with the final prediction being a voted average of all trees.
Both being tree-based algorithms, how is Random Forest different from Gradient Boosting Algorithm (GBM)?
The main difference between a random forest and GBM is the use of techniques. Random forest advances predictions using a technique called ‘bagging.’ On the other hand, GBM advances predictions with the help of a technique called ‘boosting.’
- Bagging: In bagging, we apply arbitrary sampling and we divide the dataset into N After that, we build a model by employing a single training algorithm. Following, we combine the final predictions by polling. Bagging helps increase the efficiency of the model by decreasing the variance to eschew overfitting.
- Boosting: In boosting, the algorithm tries to review and correct the inadmissible predictions at the initial iteration. After that, the algorithm’s sequence of iterations for correction continues until we get the desired prediction. Boosting assists in reducing bias and variance, both, for making the weak learners strong.
What is the role of cross-validation?
Cross-validation is a technique which is used to increase the performance of a machine learning algorithm, where the machine is fed sampled data out of the same data for a few times. The sampling is done so that the dataset is broken into small parts of the equal number of rows, and a random part is chosen as the test set, while all other parts are chosen as train sets.
What are the three stages to build the hypotheses or model in machine learning?
- Model building
- Model testing
- Applying the model
‘People who bought this, also bought…’ recommendations seen on amazon is a result of which algorithm?
The basic idea for this kind of recommendation engine comes from collaborative filtering.
Collaborative Filtering algorithm considers “User Behavior” for recommending items. They exploit behavior of other users and items in terms of transaction history, ratings, selection and purchase information. Other users behaviour and preferences over the items are used to recommend items to the new users. In this case, features of the items are not known.
What Are the Applications of Supervised Machine Learning in Modern Businesses?
Applications of supervised machine learning include:
Email Spam Detection
Here we train the model using historical data that consists of emails categorized as spam or not spam. This labeled information is fed as input to the model.
Healthcare Diagnosis
By providing images regarding a disease, a model can be trained to detect if a person is suffering from the disease or not.
Sentiment Analysis
This refers to the process of using algorithms to mine documents and determine whether they’re positive, neutral, or negative in sentiment.
Fraud Detection
Training the model to identify suspicious patterns, we can detect instances of possible fraud.
What do you mean by the ROC curve?
Receiver operating characteristics (ROC curve): ROC curve illustrates the diagnostic ability of a binary classifier. It is calculated/created by plotting True Positive against False Positive at various threshold settings. The performance metric of ROC curve is AUC (area under curve). Higher the area under the curve, better the prediction power of the model.
What is the Difference Between Supervised and Unsupervised Machine Learning?
Supervised learning – This model learns from the labeled data and makes a future prediction as output
Unsupervised learning – This model uses unlabeled input data and allows the algorithm to act on that information without guidance.
What do you mean by AUC curve?
AUC (area under curve). Higher the area under the curve, better the prediction power of the model.
What are some differences between a linked list and an array?
An array is an ordered collection of objects. A linked list is a series of objects with pointers that direct how to process them sequentially. An array assumes that every element has the same size, unlike the linked list. A linked list can more easily grow organically: an array has to be pre-defined or re-defined for organic growth. Shuffling a linked list involves changing which points direct where—meanwhile, shuffling an array is more complex and takes more memory.