Ace your data scientist interview with these top ten Machine Learning interview questions and answers-
A model with a right bias and low variance appears to perform better when the training set is small because it is less likely to overfit. For instance, Naive Bayes performs best with a large training dataset. Models that have high variance and low bias typically perform better because they can handle complex relationships.
Unsupervised learning entails training a machine learning model on a dataset without knowing the target values for each set of feature variables. As a result, we identify trends in the feature variable space and combine those that are similar.
Max-pooling in a CNN enables you to minimize computation because your feature maps are smaller after the pooling. Since you're using the maximum activation, you don't lose a lot of semantic information. There is also the idea that max-pooling offers CNNs a little bit more translation invariance.
Regression provides you with continuous results that enable you to better distinguish differences between individual points, whereas classification generates discrete values and datasets for specific categories. If you wanted your results to accurately represent the association of the data points in your dataset to specific explicit categories, you would use classification rather than regression.
A model's success is measured using the F1 score. It is a weighted average of a model's recall and precision, with results that tend towards 1 being the best and those that tend towards 0 being the worst. It may be used in classification tests where true negatives are not as important.
The decoder "decodes" the features and resizes them to the original image size in order to forecast the image segments, whereas the encoder CNN can be viewed as a feature extraction network.
A supervised machine learning technique known as "random forest" is typically applicable to classification tasks. During the training process, numerous decision trees are created. The final decision is made by the random forest according to the preferences of the majority of the trees.
A decision tree actually develops classification (or regression) models as a tree structure, splitting datasets down into ever-smaller subsets as it goes along, with branches and nodes. Both categorical and numerical data can be processed by decision trees.
A popular method for enhancing a decision tree machine learning algorithm's performance is called boosting. Each tree in Boosting is built using data from other trees that have already been evaluated. Instead of carefully fitting the dataset to produce a single, enormous decision tree, boosting involves slowly learning the dataset.
Both classification and regression can be performed using the following machine learning algorithms- Decision trees, random forests, and neural networks.
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