· a. If your data is labeled, but you only have a limited amount, you should use a classifier with high bias (for example, Naive Bayes). I''m guessing this is because a higher-bias classifier will have lower variance, which is good because of the small amount of data. b.
· There are tradeoffs between the types of errors that a machine learning practitioner must consider and often choose to accept. For binary classification problems, there …
Most machine learning techniques can handle mixed-type data. Tree based methods (such as AdaBoost and Random Forests) do well with this type of data. The more important issue is actually the dimensionality, about which you are correct to be concerned.
Aside from its simplicity and ease of interpretation, here are the other advantaged of using decision tree fo classification in machine learning. Considered a white box type of ML algorithm, decision tree uses an internal decision-making logic; this means that the acquired knowledge from a data set can be easily extracted in a readable form which is not a feature of black box algorithms such ...
· In this example, this model was trained on an imbalanced dataset and even the test dataset is imbalanced. The Accuracy metric has a score of 72% which might give us the impression that our model is doing a good job at the classification. But, look closer, this ...
Usually these are the ones on which a classifier is uncertain of the correct classification. This can be effective in reducing annotation costs by a factor of 2-4, but has the problem that the good documents to label to train one type of classifier often are not the good
· Answer: A lot of machine learning interview questions of this type will involve the implementation of machine learning models to a company''s problems. You''ll have to research the company and its industry in-depth, especially the revenue drivers the company has, and the types of users the company takes on in the context of the industry it''s in.
Answer (1 of 5): There are a lot of algorithms that people used for image classification before CNN became popular. People used to create features from images and then feed those features into some classification algorithm like SVM. Some algorithm also used the
· Two broad categories in machine learning are supervised and unsupervised learning. Classification and clustering are examples of each of those respectively, and in this post I will go over the differences between them and when you might use them.
If we have a good classifier, the cumulative gain should be above the baseline, while the negative cumulative gain should be below it as it happens for our example. Supposing that our example was a marketing campaign, the curve shows that we would have gotten all the positive responses by calling only 60% of the population with the highest scores.
An example of classification problem can be the spam detection in emails. There can be only two categories of output, "spam" and "no spam"; hence this is a binary type classification. To implement this classification, we first need to train the classifier.
In such a kind of classification, dependent variable can have 3 or more possible ordered types or the types having a quantitative significance. For example, these variables may represent "poor" or "good", "very good", "Excellent" and each category can have the scores like 0,1,2,3.
· Introduction Machine Learning algorithms are generally categorized based upon the type of output variable and the type of problem that needs to be addressed. These algorithms are broadly divided into three types i.e. Regression, Clustering, and Classification.
· There are many different types of classification algorithms for modeling classification predictive modeling problems. There is no good theory on how to map algorithms onto problem types; instead, it is generally recommended that a practitioner use controlled experiments and discover which algorithm and algorithm configuration results in the best performance for a given classification task.
As for as, SVM is concerned, it is a suitable classifier in following cases: 1) When number of features (variables) and number of training data is very large (say millions of features and millions ...
· Machine learning algorithms are key for anyone who''s interested in the data science field. Here''s an introduction to ten of the most fundamental algorithms. Interest in learning machine learning has skyrocketed in the years since Harvard Business Review article named ''Data Scientist'' the ''Sexiest job of the 21st century''. ''. But if you''re just starting out in machine learning, it ...
· In machine learning, there''s something called the "No Free Lunch" theorem which means no one algorithm works well for every problem. This is widely applicable in Prediction Models where we train our dataset on an algorithm and later use the trained model for
· Machine Learning. Binary Classification is a type of classification model that have two label of classes. For example an email spam detection model contains two label of classes as spam or not spam. Most of the times the tasks of binary classification includes one label in a normal state, and another label in an abnormal state.
6. Multi-label classification: A multi-label classification is a classification where a data object can be assigned multiple labels or output classes. Classification Algorithms in R There are various classifiers or classification algorithms in machine learning and R 1.
· Types of SVM. Linear SVM : Linear SVM is used for data that are linearly separable i.e. for a dataset that can be categorized into two categories by utilizing a single straight line. Such data points are termed as linearly separable data, and the classifier is used described as a Linear SVM classifier.
· Classification is a natural language processing task that depends on machine learning algorithms.There are many different types of classification tasks that you can perform, the most popular being sentiment analysis.Each task often requires a different algorithm
4. Random Forest Classifier. It is one of the most popular machine learning classification algorithms out there. As the name suggests Random Forest algorithm is about creating trees in a forest and make it random. The more trees in the forest, the more accurate the result is.
· Over-fitting is a common problem in machine learning which can occur in most models. k-fold cross-validation can be conducted to verify that the model is not over-fitted. In this method, the data-set is randomly partitioned into k mutually exclusive subsets, each approximately equal size and one is kept for testing while others are used for training.
SVM in Machine Learning – An exclusive guide on SVM algorithms. Support Vector Machine is a classifier algorithm, that is, it is a classification-based technique. It is very useful if the data size is less. This algorithm is not effective for large sets of data. For …
Classifier comparison. ¶. A comparison of a several classifiers in scikit-learn on synthetic datasets. The point of this example is to illustrate the nature of decision boundaries of different classifiers. This should be taken with a grain of salt, as the intuition conveyed by …