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Data Science is a combination of algorithms, tools, and machine learning technique which helps you to find common hidden patterns from the given raw data.
In the sampling process, there are three types of biases, which are:
Logistic Regression is also called as the logit model. It is a method to forecast the binary outcome from a linear combination of predictor variables.
A decision tree is a popular supervised machine learning algorithm. It is mainly used for Regression and Classification. It allows breaks down a dataset into smaller subsets. The decision tree can able to handle both categorical and numerical data.
It is a subclass of information filtering techniques. It helps you to predict the preferences or ratings which users likely to give to a product.
Prior probability is the proportion of the dependent variable in the data set while the likelihood is the probability of classifying a given observant in the presence of some other variable.
Resampling is done in below-given cases:
Three disadvantages of the linear model are:
The power analysis is an integral part of the experimental design. It helps you to determine the sample size requires to find out the effect of a given size from a cause with a specific level of assurance. It also allows you to deploy a particular probability in a sample size constraint.
Bias is an error introduced in your model because of the oversimplification of a machine learning algorithm." It can lead to underfitting.
Collaborative filtering used to search for correct patterns by collaborating viewpoints, multiple data sources, and various agents.
Linear regression is a statistical programming method where the score of a variable 'A' is predicted from the score of a second variable 'B'. B is referred to as the predictor variable and A as the criterion variable.
The Naive Bayes Algorithm model is based on the Bayes Theorem. It describes the probability of an event. It is based on prior knowledge of conditions which might be related to that specific event.:
AB testing used to conduct random experiments with two variables, A and B. The goal of this testing method is to find out changes to a web page to maximize or increase the outcome of a strategy.
They are not many differences, but both of these terms are used in different contexts. Mean value is generally referred to when you are discussing a probability distribution whereas expected value is referred to in the context of a random variable.
Eigenvectors are for understanding linear transformations. Data scientist need to calculate the eigenvectors for a covariance matrix or correlation. Eigenvalues are the directions along using specific linear transformation acts by compressing, flipping, or stretching.
Artificial Neural networks (ANN) are a special set of algorithms that have revolutionized machine learning. It helps you to adapt according to changing input. So the network generates the best possible result without redesigning the output criteria.
Cross-validation is a validation technique for evaluating how the outcomes of statistical analysis will generalize for an Independent dataset. This method is used in backgrounds where the objective is forecast, and one needs to estimate how accurately a model will accomplish.
Random forest is a machine learning method which helps you to perform all types of regression and classification tasks. It is also used for treating missing values and outlier values….