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C3700 | A perceptron is a neural network unit (an artificial neuron) that does certain computations to detect features or business intelligence in the input data. Perceptron was introduced by Frank Rosenblatt in 1957. A Perceptron is an algorithm for supervised learning of binary classifiers. | |
C3701 | The decision rule is: Reject H0 if Z < 1.645. The decision rule is: Reject H0 if Z < -1.960 or if Z > 1.960. The complete table of critical values of Z for upper, lower and two-tailed tests can be found in the table of Z values to the right in "Other Resources." | |
C3702 | Face recognition systems use computer algorithms to pick out specific, distinctive details about a person's face. These details, such as distance between the eyes or shape of the chin, are then converted into a mathematical representation and compared to data on other faces collected in a face recognition database. | |
C3703 | A box plot (also known as box and whisker plot) is a type of chart often used in explanatory data analysis to visually show the distribution of numerical data and skewness through displaying the data quartiles (or percentiles) and averages. | |
C3704 | Descriptive statistics describes data (for example, a chart or graph) and inferential statistics allows you to make predictions (“inferences”) from that data. With inferential statistics, you take data from samples and make generalizations about a population. | |
C3705 | Categorical variables require special attention in regression analysis because, unlike dichotomous or continuous variables, they cannot by entered into the regression equation just as they are. Instead, they need to be recoded into a series of variables which can then be entered into the regression model. | |
C3706 | False negatives — that is, a test that says you don't have the virus when you actually do have the virus — may occur. | |
C3707 | To define an optimal hyperplane we need to maximize the width of the margin (w). In this situation SVM finds the hyperplane that maximizes the margin and minimizes the misclassifications. The algorithm tries to maintain the slack variable to zero while maximizing margin. | |
C3708 | A learning curve plots the score over varying numbers of training samples, while a validation curve plots the score over a varying hyper parameter. The learning curve is a tool for finding out if an estimator would benefit from more data, or if the model is too simple (biased). | |
C3709 | R-squared is a goodness-of-fit measure for linear regression models. This statistic indicates the percentage of the variance in the dependent variable that the independent variables explain collectively. For instance, small R-squared values are not always a problem, and high R-squared values are not necessarily good! | |
C3710 | Word sense disambiguation, in natural language processing (NLP), may be defined as the ability to determine which meaning of word is activated by the use of word in a particular context. Lexical ambiguity, syntactic or semantic, is one of the very first problem that any NLP system faces. | |
C3711 | “Covariance” indicates the direction of the linear relationship between variables. “Correlation” on the other hand measures both the strength and direction of the linear relationship between two variables. Correlation is a function of the covariance. | |
C3712 | The clear sign of a machine learning overfitting is if its error on testing set is much greater than the error on training set. For instance if the model accuracy for train data is 85% and the accuracy for test/validation data is 65% then its very obvious that the model has overlearned and you should check that. | |
C3713 | If there is no relationship between X and Y, the best guess for all values of X is the mean of Y. At any rate, the regression line always passes through the means of X and Y. This means that, regardless of the value of the slope, when X is at its mean, so is Y. | |
C3714 | A statistic is biased if the long-term average value of the statistic is not the parameter it is estimating. More formally, a statistic is biased if the mean of the sampling distribution of the statistic is not equal to the parameter. Therefore the sample mean is an unbiased estimate of μ. | |
C3715 | Performance wise SVMs using the radial basis function kernel are more likely to perform better as they can handle non-linearities in the data. Naive Bayes performs best when the features are independent of each other which often does not happen in real. | |
C3716 | The Word2Vec Model This model was created by Google in 2013 and is a predictive deep learning based model to compute and generate high quality, distributed and continuous dense vector representations of words, which capture contextual and semantic similarity. | |
C3717 | Some applications of unsupervised machine learning techniques are: Clustering automatically split the dataset into groups base on their similarities. Anomaly detection can discover unusual data points in your dataset. It is useful for finding fraudulent transactions. | |
C3718 | Latent semantic analysis (LSA) is a mathematical method for computer modeling and simulation of the meaning of words and passages by analysis of representative corpora of natural text. LSA closely approximates many aspects of human language learning and understanding. | |
C3719 | Parametric tests are those that make assumptions about the parameters of the population distribution from which the sample is drawn. This is often the assumption that the population data are normally distributed. Non-parametric tests are “distribution-free” and, as such, can be used for non-Normal variables. | |
C3720 | Association rule mining is a procedure which aims to observe frequently occurring patterns, correlations, or associations from datasets found in various kinds of databases such as relational databases, transactional databases, and other forms of repositories. | |
C3721 | The term "negative binomial" is likely due to the fact that a certain binomial coefficient that appears in the formula for the probability mass function of the distribution can be written more simply with negative numbers. | |
C3722 | As the formula shows, the standard score is simply the score, minus the mean score, divided by the standard deviation. | |
C3723 | In simple words, Instance refers to the copy of the object at a particular time whereas object refers to the memory address of the class. | |
C3724 | 1:5313:32Suggested clip · 105 secondsDimensional Analysis Explained! - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C3725 | Type 1 error, in statistical hypothesis testing, is the error caused by rejecting a null hypothesis when it is true. Type II error is the error that occurs when the null hypothesis is accepted when it is not true. Type I error is equivalent to false positive. | |
C3726 | When class intervals are unequal, we take Frequency Density on Y axis. Based on this find the tallest class interval and follow the regular method of joining top corners of tallest column to the top corners of the opposite adjacent columns. The X-coordinate of Intersection point would give value of Mode. | |
C3727 | Decision trees are a classic machine learning technique. The basic intuition behind a decision tree is to map out all possible decision paths in the form of a tree. By Narendra Nath Joshi, Carnegie Mellon. The basic intuition behind a decision tree is to map out all possible decision paths in the form of a tree. | |
C3728 | Decision theory is an interdisciplinary approach to arrive at the decisions that are the most advantageous given an uncertain environment. Decision theory brings together psychology, statistics, philosophy, and mathematics to analyze the decision-making process. | |
C3729 | No, logistic regression does not require any particular distribution for the independent variables. They can be normal, skewed, categorical or whatever. No regression method makes assumptions about the shape of the distribution of either the IVs or the DV. | |
C3730 | The scale-invariant feature transform (SIFT) is an algorithm used to detect and describe local features in digital images. The descriptors are supposed to be invariant against various transformations which might make images look different although they represent the same object(s). | |
C3731 | The beta distribution of the first kind, usually written in terms of the incom- plete beta function, can be used to model the distribution of measurements whose values all lie between zero and one. It can also be used to model the distribution for the probability of occurrence of some discrete event. | |
C3732 | Therefore, a low test–retest reliability correlation might be indicative of a measure with low reliability, of true changes in the persons being measured, or both. That is, in the test–retest method of estimating reliability, it is not possible to separate the reliability of measure from its stability. | |
C3733 | In machine learning, the term inductive bias refers to a set of (explicit or implicit) assumptions made by a learning algorithm in order to perform induction, that is, to generalize a finite set of observation (training data) into a general model of the domain. | |
C3734 | For example, let's say a child received a scaled score of 8, with a 95% confidence interval range of 7-9. This means that with high certainty, the child's true score lies between 7 and 9, even if the received score of 8 is not 100% accurate. | |
C3735 | Sentiment analysis (also known as opinion mining or emotion AI) refers to the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. | |
C3736 | Yes, we can apply logistic regression on 3 classification problem, We can use One Vs all method for 3 class classification in logistic regression. | |
C3737 | The Regression Fallacy occurs when one mistakes regression to the mean, which is a statistical phenomenon, for a causal relationship. For example, if a tall father were to conclude that his tall wife committed adultery because their children were shorter, he would be committing the regression fallacy. | |
C3738 | Cumulative relative frequency distribution–showsthe proportionof items with values less than orequal to the upper limit of each class. Cumulative DistributionsCumulative percent frequency distribution–showsthe percentageof items with values less than orequal to the upper limit of each class. | |
C3739 | According to research conducted at Cornell University, researchers state that “the traditional phrase-based translation system which consists of many small sub-components that are tuned separately, neural machine translation attempts to build and train a single, large neural network that reads a sentence and outputs a | |
C3740 | The difference is simple and conceptual. A class is a template for objects. An object is a member or an "instance" of a class. An object has a state in which all of its properties have values that you either explicitly define or that are defined by default settings. | |
C3741 | Quantiles are points in a distribution that relate to the rank order of values in that distribution. For a sample, you can find any quantile by sorting the sample. The middle value of the sorted sample (middle quantile, 50th percentile) is known as the median. | |
C3742 | The cumulative distribution function (CDF) calculates the cumulative probability for a given x-value. Use the CDF to determine the probability that a random observation that is taken from the population will be less than or equal to a certain value. | |
C3743 | The Effect Smart Market is a peer-to-peer marketplace for artificial intelligence algorithms. It offers a web-based service that opens up the global AI market to all. It is the easiest way to buy, sell and trade AI-powered solutions with fast and reliable API access. | |
C3744 | Linearity assumption This can be done by visually inspecting the scatter plot between each predictor and the logit values. The smoothed scatter plots show that variables glucose, mass, pregnant, pressure and triceps are all quite linearly associated with the diabetes outcome in logit scale. | |
C3745 | The three main metrics used to evaluate a classification model are accuracy, precision, and recall. Accuracy is defined as the percentage of correct predictions for the test data. It can be calculated easily by dividing the number of correct predictions by the number of total predictions. | |
C3746 | Seriously, the p value is literally a confounded index because it reflects both the size of the underlying effect and the size of the sample. Hence any information included in the p value is ambiguous (Lang et al. 1998). The smaller the sample, the less likely the result will be statistically significant. | |
C3747 | Linear models, generalized linear models, and nonlinear models are examples of parametric regression models because we know the function that describes the relationship between the response and explanatory variables. If the relationship is unknown and nonlinear, nonparametric regression models should be used. | |
C3748 | For a binary classification like our example, the typical loss function is the binary cross-entropy / log loss. | |
C3749 | A matrix A is symmetric if it is equal to its transpose, i.e., A=AT. A matrix A is symmetric if and only if swapping indices doesn't change its components, i.e., aij=aji. | |
C3750 | Advantages of Systematic SamplingEasy to Execute and Understand.Control and Sense of Process.Clustered Selection Eliminated.Low Risk Factor.Assumes Size of Population Can Be Determined.Need for Natural Degree of Randomness.Greater Risk of Data Manipulation. | |
C3751 | The Z score is a test of statistical significance that helps you decide whether or not to reject the null hypothesis. The p-value is the probability that you have falsely rejected the null hypothesis. Z scores are measures of standard deviation. Both statistics are associated with the standard normal distribution. | |
C3752 | Data augmentation is a technique to artificially create new training data from existing training data. This is done by applying domain-specific techniques to examples from the training data that create new and different training examples. The intent is to expand the training dataset with new, plausible examples. | |
C3753 | While the variance and the standard error of the mean are different estimates of variability, one can be derived from the other. Multiply the standard error of the mean by itself to square it. This step assumes that the standard error is a known quantity. | |
C3754 | Mutual information is a distance between two probability distributions. Correlation is a linear distance between two random variables. If you are working with variables that are smooth, correlation may tell you more about them; for instance if their relationship is monotonic. | |
C3755 | In statistical hypothesis testing, the null distribution is the probability distribution of the test statistic when the null hypothesis is true. For example, in an F-test, the null distribution is an F-distribution. Null distribution is a tool scientists often use when conducting experiments. | |
C3756 | The Basics of a One-Tailed Test Hypothesis testing is run to determine whether a claim is true or not, given a population parameter. A test that is conducted to show whether the mean of the sample is significantly greater than and significantly less than the mean of a population is considered a two-tailed test. | |
C3757 | The primary reason skew is important is that analysis based on normal distributions incorrectly estimates expected returns and risk. Knowing that the market has a 70% probability of going up and a 30% probability of going down may appear helpful if you rely on normal distributions. | |
C3758 | The correlation coefficient is the specific measure that quantifies the strength of the linear relationship between two variables in a correlation analysis. The coefficient is what we symbolize with the r in a correlation report. | |
C3759 | A Markov chain is a mathematical system that experiences transitions from one state to another according to certain probabilistic rules. The defining characteristic of a Markov chain is that no matter how the process arrived at its present state, the possible future states are fixed. | |
C3760 | Multiple regression is an extension of simple linear regression. It is used when we want to predict the value of a variable based on the value of two or more other variables. The variable we want to predict is called the dependent variable (or sometimes, the outcome, target or criterion variable). | |
C3761 | Univariate statistics summarize only one variable at a time. Bivariate statistics compare two variables. Multivariate statistics compare more than two variables. | |
C3762 | Discriminant analysis is a versatile statistical method often used by market researchers to classify observations into two or more groups or categories. In other words, discriminant analysis is used to assign objects to one group among a number of known groups. | |
C3763 | Machine learning algorithms find natural patterns in data that generate insight and help you make better decisions and predictions. They are used every day to make critical decisions in medical diagnosis, stock trading, energy load forecasting, and more. | |
C3764 | Supervised learning is the category of machine learning algorithms that require annotated training data. For instance, if you want to create an image classification model, you must train it on a vast number of images that have been labeled with their proper class. “[Deep learning] is not supervised learning. | |
C3765 | Squared loss is a loss function that can be used in the learning setting in which we are predicting a real-valued variable y given an input variable x. | |
C3766 | The Cox (proportional hazards or PH) model (Cox, 1972) is the most commonly used multivariate approach for analysing survival time data in medical research. It is a survival analysis regression model, which describes the relation between the event incidence, as expressed by the hazard function and a set of covariates. | |
C3767 | In machine learning, instance-based learning (sometimes called memory-based learning) is a family of learning algorithms that, instead of performing explicit generalization, compares new problem instances with instances seen in training, which have been stored in memory. | |
C3768 | Confidence intervals and hypothesis tests are similar in that they are both inferential methods that rely on an approximated sampling distribution. Confidence intervals use data from a sample to estimate a population parameter. Hypothesis tests use data from a sample to test a specified hypothesis. | |
C3769 | A residual plot is a graph that shows the residuals on the vertical axis and the independent variable on the horizontal axis. If the points in a residual plot are randomly dispersed around the horizontal axis, a linear regression model is appropriate for the data; otherwise, a nonlinear model is more appropriate. | |
C3770 | A variable is said to be continuous if it can assume an infinite number of real values. Examples of a continuous variable are distance, age and temperature. The measurement of a continuous variable is restricted by the methods used, or by the accuracy of the measuring instruments. | |
C3771 | Elastic net regularization adds an additional ridge regression-like penalty which improves performance when the number of predictors is larger than the sample size, allows the method to select strongly correlated variables together, and improves overall prediction accuracy. | |
C3772 | A class is a blueprint which you use to create objects. An object is an instance of a class - it's a concrete 'thing' that you made using a specific class. So, 'object' and 'instance' are the same thing, but the word 'instance' indicates the relationship of an object to its class. | |
C3773 | In the context of machine learning, an embedding is a low-dimensional, learned continuous vector representation of discrete variables into which you can translate high-dimensional vectors. Generally, embeddings make ML models more efficient and easier to work with, and can be used with other models as well. | |
C3774 | A probability-predicting regression model can be used as part of a classifier by imposing a decision rule - for example, if the probability is 50% or more, decide it's a cat. There are also "true" classification algorithms, such as SVM, which only predict an outcome and do not provide a probability. | |
C3775 | Average can simply be defined as the sum of all the numbers divided by the total number of values. Average is usually present as mean or arithmetic mean. Mean is simply a method of describing the average of the sample. The arithmetic mean is considered as a form of average. | |
C3776 | In statistics, a Multimodal distribution is a probability distribution with two different modes, may also be referred to as a bimodal distribution. These appear as distinct peaks (local maxima) in the probability density function, as shown in Figures 1 and 2. | |
C3777 | Given two competing hypotheses and some relevant data, Bayesian hypothesis testing begins by specifying separate prior distributions to quantitatively describe each hypothesis. The combination of the likelihood function for the observed data with each of the prior distributions yields hypothesis-specific models. | |
C3778 | To train a deep neural network to classify sequence data, you can use an LSTM network. An LSTM network enables you to input sequence data into a network, and make predictions based on the individual time steps of the sequence data. | |
C3779 | Linear discriminant analysis (LDA), normal discriminant analysis (NDA), or discriminant function analysis is a generalization of Fisher's linear discriminant, a method used in statistics, pattern recognition, and other fields, to find a linear combination of features that characterizes or separates two or more classes | |
C3780 | The predicted value of y ("ˆy ") is sometimes referred to as the "fitted value" and is computed as ˆyi=b0+b1xi y ^ i = b 0 + b 1 x i . | |
C3781 | The weight of a fire fighter would be an example of a continuous variable; since a fire fighter's weight could take on any value between 150 and 250 pounds. | |
C3782 | It shows that the dependent variable (IQ) is significantly correlated with all the variables included in the analysis, except for sex, and physical attractiveness is more strongly associated with general intelligence than any other variable. | |
C3783 | Dynamic Programming is used to obtain the optimal solution. In Dynamic Programming, we choose at each step, but the choice may depend on the solution to sub-problems. 2. In a greedy Algorithm, we make whatever choice seems best at the moment and then solve the sub-problems arising after the choice is made. | |
C3784 | Artificial intelligence is based on the principle that human intelligence can be defined in a way that a machine can easily mimic it and execute tasks, from the most simple to those that are even more complex. The goals of artificial intelligence include learning, reasoning, and perception. | |
C3785 | A Z score is the number of standard deviations a given result is above (positive score) or below (negative score) the age- and sex-adjusted population mean. Results that are within the IGF-1 reference interval will have a Z score between -2.0 and +2.0. | |
C3786 | There are three basic concepts in reinforcement learning: state, action, and reward. The state describes the current situation. For a robot that is learning to walk, the state is the position of its two legs. For a Go program, the state is the positions of all the pieces on the board. | |
C3787 | 1. A pattern recognition technique that is used to categorize a huge number of data into different classes. | |
C3788 | The Vanishing Gradient problem, is when the signal parsing of SGD (Stochastic Gradient Descent) or other forms of GD (Gradient Descent) - becomes so small - or the signal becomes so approximative small - that the signal registers as 0.Think of the signal as balancing between two modes:1 and 0.More items | |
C3789 | A joint probability distribution shows a probability distribution for two (or more) random variables. Instead of events being labeled A and B, the norm is to use X and Y. The formal definition is: f(x,y) = P(X = x, Y = y) The whole point of the joint distribution is to look for a relationship between two variables. | |
C3790 | The dependent variable is the variable that is being measured or tested in an experiment. For example, in a study looking at how tutoring impacts test scores, the dependent variable would be the participants' test scores, since that is what is being measured. | |
C3791 | Variance plays a major role in interpreting data in statistics. The most common application of variance is in polls. For opinion polls, the data gathering agencies cannot invest in collecting data from the entire population. | |
C3792 | Every probability pi is a number between 0 and 1, and the sum of all the probabilities is equal to 1. Examples of discrete random variables include: The number of eggs that a hen lays in a given day (it can't be 2.3) The number of people going to a given soccer match. | |
C3793 | Role of Scaling is mostly important in algorithms that are distance based and require Euclidean Distance. Random Forest is a tree-based model and hence does not require feature scaling. | |
C3794 | Many classification and clustering methods depend upon some measure of distance and similarity or distance between objects. If they do, then they can use cosine similarity. Similarity measures are not machine learning algorithm per se, but they play an integral part. | |
C3795 | Principal Component Analysis (PCA) is used to explain the variance-covariance structure of a set of variables through linear combinations. It is often used as a dimensionality-reduction technique. | |
C3796 | Deep Belief NetworksTrain the first layer as an RBM that models the raw input. Use that first layer to obtain a representation of the input that will be used as data for the second layer. Train the second layer as an RBM, taking the transformed data (samples or mean activations) as training examples (for the visible layer of that RBM).More items | |
C3797 | Receiver Operating Characteristics (ROC) Curve For classification models, there are many other evaluation methods like Gain and Lift charts, Gini coefficient etc. But the in depth knowledge about the confusion matrix can help to evaluate any classification model very effectively. | |
C3798 | If your data are missing completely at random, you could consider listwise deletion: just remove the cases with missing values from your analysis. In addition to decision trees, logistic regression is the workhorse in the modelling in order to forecast the occurrence of an event. | |
C3799 | Matrix factorization using the alternating least squares algorithm for collaborative filtering. Alternating least squares (ALS) is an optimization technique to solve the matrix factorization problem. This technique achieves good performance and has proven relatively easy to implement. |
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