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C1900 | Reinforcement learning will be the next big thing in data science in 2019. The potential value in using RL in proactive analytics and AI is enormous, but it also demands a greater skillset to master. | |
C1901 | Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses. The most common unsupervised learning method is cluster analysis, which is used for exploratory data analysis to find hidden patterns or grouping in data. | |
C1902 | In probability theory, a log-normal (or lognormal) distribution is a continuous probability distribution of a random variable whose logarithm is normally distributed. A random variable which is log-normally distributed takes only positive real values. | |
C1903 | It provides an optimal move for the player assuming that opponent is also playing optimally. Mini-Max algorithm uses recursion to search through the game-tree. This Algorithm computes the minimax decision for the current state. In this algorithm two players play the game, one is called MAX and other is called MIN. | |
C1904 | Any point directly on the y-axis has an X value of 0. Multiple Choice: In a simple Linear regression problem, r and b1. Explanation: r= correlation coefficient and b1= slope. If we have a downward sloping trend-line then that means we have a negative (or inverse) correlation coefficient. | |
C1905 | Applications of Dimensional AnalysisTo check the consistency of a dimensional equation.To derive the relation between physical quantities in physical phenomena.To change units from one system to another. | |
C1906 | μˆP and a standard deviation. σˆP. Thus the population proportion p is the same as the mean μ of the corresponding population of zeros and ones. In the same way the sample proportion ˆp is the same as the sample mean ˉx. | |
C1907 | There are four main types of probability sample.Simple random sampling. In a simple random sample, every member of the population has an equal chance of being selected. Systematic sampling. Stratified sampling. Cluster sampling. | |
C1908 | 8 Common Data Structures every Programmer must know. A quick introduction to 8 commonly used data structures. Arrays. An array is a structure of fixed-size, which can hold items of the same data type. Linked Lists. Stacks. Queues. Hash Tables. Trees. Heaps.More items | |
C1909 | Learning Rate and Gradient Descent Specifically, the learning rate is a configurable hyperparameter used in the training of neural networks that has a small positive value, often in the range between 0.0 and 1.0. The learning rate controls how quickly the model is adapted to the problem. | |
C1910 | The Area Under the Curve (AUC) is the measure of the ability of a classifier to distinguish between classes and is used as a summary of the ROC curve. The higher the AUC, the better the performance of the model at distinguishing between the positive and negative classes. | |
C1911 | It is also called flat clustering algorithm. The number of clusters identified from data by algorithm is represented by 'K' in K-means. In this algorithm, the data points are assigned to a cluster in such a manner that the sum of the squared distance between the data points and centroid would be minimum. | |
C1912 | Supervised learning is simply a process of learning algorithm from the training dataset. Unsupervised learning is modeling the underlying or hidden structure or distribution in the data in order to learn more about the data. Unsupervised learning is where you only have input data and no corresponding output variables. | |
C1913 | Answer. P(A ∩ B) and P(A|B) are very closely related. Their only difference is that the conditional probability assumes that we already know something -- that B is true. For P(A|B), however, we will receive a probability between 0, if A cannot happen when B is true, and P(B), if A is always true when B is true. | |
C1914 | From Wikipedia, the free encyclopedia. In machine learning, the radial basis function kernel, or RBF kernel, is a popular kernel function used in various kernelized learning algorithms. In particular, it is commonly used in support vector machine classification. | |
C1915 | The estimation of distribution algorithm (EDA) aims to explicitly model the probability distribution of the quality solutions to the underlying problem. By iterative filtering for quality solution from competing ones, the probability model eventually approximates the distribution of global optimum solutions. | |
C1916 | 2:266:36Suggested clip · 120 secondsAn Easy Rule to Setting Up the Null & Alternate Hypotheses YouTubeStart of suggested clipEnd of suggested clip | |
C1917 | You can use a scatter plot to analyze trends in your data and to help you to determine whether or not there is a relationship between two variables. If the points on the scatter plot seem to form a line that slants down from left to right, there is a negative relationship or negative correlation between the variables. | |
C1918 | agreement worse than expected | |
C1919 | The difference is a matter of design. In the test of independence, observational units are collected at random from a population and two categorical variables are observed for each unit. In the goodness-of-fit test there is only one observed variable. | |
C1920 | In probability, we say two events are independent if knowing one event occurred doesn't change the probability of the other event. So the result of a coin flip and the day being Tuesday are independent events; knowing it was a Tuesday didn't change the probability of getting "heads." | |
C1921 | Convolutional Neural Networks (CNNs) is the most popular neural network model being used for image classification problem. The big idea behind CNNs is that a local understanding of an image is good enough. | |
C1922 | Linear regression is commonly used for predictive analysis and modeling. For example, it can be used to quantify the relative impacts of age, gender, and diet (the predictor variables) on height (the outcome variable). | |
C1923 | Multi-class Classification using Decision Tree, Random Forest and Extra Trees Algorithm in Python: An End-To-End Data Science Recipe — 016. a) Different types of Machine Learning problems. i) How to implement Decision Tree, Random Forest and Extra Tree Algorithms for Multiclass Classification in Python. | |
C1924 | Bayesian networks encode the dependencies and independencies between variables. Under the causal Markov assumption, each variable in a Bayesian network is independent of its ancestors given the values of its parents. | |
C1925 | How to find the mean of the probability distribution: StepsStep 1: Convert all the percentages to decimal probabilities. For example: Step 2: Construct a probability distribution table. Step 3: Multiply the values in each column. Step 4: Add the results from step 3 together. | |
C1926 | In class limit, the upper extreme value of the first class interval and the lower extreme value of the next class interval will not be equal. In class boundary, the upper extreme value of the first class interval and the lower extreme value of the next class interval will be equal. | |
C1927 | Because it is a cost function, a lower Brier score indicates more accurate predictions while a higher Brier score indicates less accurate predictions. In its most common formulation, the best and worst possible Brier scores are 0 and 1 respectively. | |
C1928 | The hazard function (also called the force of mortality, instantaneous failure rate, instantaneous death rate, or age-specific failure rate) is a way to model data distribution in survival analysis. The function is defined as the instantaneous risk that the event of interest happens, within a very narrow time frame. | |
C1929 | A one-sided argument (also known as card stacking, stacking the deck, ignoring the counterevidence, slanting, and suppressed evidence) is an informal fallacy that occurs when only the reasons supporting a proposition are supplied, while all reasons opposing it are omitted. | |
C1930 | Linear regression can only be used when one has two continuous variables—an independent variable and a dependent variable. The independent variable is the parameter that is used to calculate the dependent variable or outcome. A multiple regression model extends to several explanatory variables. | |
C1931 | Deep reinforcement learning is a promising combination between two artificial intelligence techniques: reinforcement learning, which uses sequential trial and error to learn the best action to take in every situation, and deep learning, which can evaluate complex inputs and select the best response. | |
C1932 | The "mean" is the "average" you're used to, where you add up all the numbers and then divide by the number of numbers. The "median" is the "middle" value in the list of numbers. If no number in the list is repeated, then there is no mode for the list. | |
C1933 | The parameters of a logistic regression model can be estimated by the probabilistic framework called maximum likelihood estimation.This tutorial is divided into four parts; they are:Logistic Regression.Logistic Regression and Log-Odds.Maximum Likelihood Estimation.Logistic Regression as Maximum Likelihood. | |
C1934 | Summary: Chaos theory is a mathematical theory that can be used to explain complex systems such as weather, astronomy, politics, and economics. Although many complex systems appear to behave in a random manner, chaos theory shows that, in reality, there is an underlying order that is difficult to see. | |
C1935 | Hello every one, We know that Pearson linear correlation coefficient gives the strength of linear relationship, while Spearman rank correlation coefficient gives the strength of monotonic relationship between two variables. | |
C1936 | Theoretical probability is a method to express the likelihood that something will occur. It is calculated by dividing the number of favorable outcomes by the total possible outcomes. The result is a ratio that can be expressed as a fraction (like 2/5), or a decimal (like . | |
C1937 | In statistics, an efficient estimator is an estimator that estimates the quantity of interest in some “best possible” manner. | |
C1938 | Unsupervised: All data is unlabeled and the algorithms learn to inherent structure from the input data. Semi-supervised: Some data is labeled but most of it is unlabeled and a mixture of supervised and unsupervised techniques can be used. | |
C1939 | In statistics universe (population) refers to an aggregate of all items about which we want to obtain information. Sample is only the part of the population or the universe. This part must represent the characteristics of universe. | |
C1940 | A t-test tests a null hypothesis about two means; most often, it tests the hypothesis that two means are equal, or that the difference between them is zero. A chi-square test tests a null hypothesis about the relationship between two variables. | |
C1941 | Bayesian inference is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available. Bayesian inference is an important technique in statistics, and especially in mathematical statistics. | |
C1942 | The sign of a regression coefficient tells you whether there is a positive or negative correlation between each independent variable the dependent variable. A positive coefficient indicates that as the value of the independent variable increases, the mean of the dependent variable also tends to increase. | |
C1943 | 7 steps to improve your data structure and algorithm skillsStep 1: Understand Depth vs. Breadth.Step 2: Start the Depth-First Approach—make a list of core questions.Step 3: Master each data structure.Step 4: Spaced Repetition.Step 5: Isolate techniques that are reused. Isolate actual code blocks.Step 6: Now, it's time for Breadth.Step 7: Practice on paper. | |
C1944 | An odds ratio (OR) is a measure of association between an exposure and an outcome. The OR represents the odds that an outcome will occur given a particular exposure, compared to the odds of the outcome occurring in the absence of that exposure. | |
C1945 | The T distribution is similar to the normal distribution, just with fatter tails. Both assume a normally distributed population. T distributions have higher kurtosis than normal distributions. The probability of getting values very far from the mean is larger with a T distribution than a normal distribution. | |
C1946 | This is referred to as the joint probability of X = x and Y = y. If X and Y are discrete random variables, the function given by f (x, y) = P(X = x, Y = y) for each pair of values (x, y) within the range of X is called the joint probability distribution of X and Y . | |
C1947 | Standard deviation is the deviation from the mean, and a standard deviation is nothing but the square root of the variance. Mean is an average of all set of data available with an investor or company. Standard deviation used for measuring the volatility of a stock. Standard deviation is easier to picture and apply. | |
C1948 | Convolutional Neural Networks (ConvNets or CNNs) are a category of Neural Networks that have proven very effective in areas such as image recognition and classification. ConvNets have been successful in identifying faces, objects and traffic signs apart from powering vision in robots and self driving cars. | |
C1949 | Guidelines for comparing boxplotsCompare the respective medians, to compare location.Compare the interquartile ranges (that is, the box lengths), to compare dispersion.Look at the overall spread as shown by the adjacent values. Look for signs of skewness. Look for potential outliers. | |
C1950 | ASUS EZ Flash 3 allows you to download and update to the latest BIOS through the Internet without having to use a bootable disk or an OS-based utility. | |
C1951 | Skewed data often occur due to lower or upper bounds on the data. That is, data that have a lower bound are often skewed right while data that have an upper bound are often skewed left. Skewness can also result from start-up effects. | |
C1952 | 0:005:54Suggested clip · 111 secondsInterpreting correlation coefficients in a correlation matrix - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C1953 | Now to draw calibration plot the following steps are followed.Create a data set with two columns that are actual label and its predicted probability given by the model.Sort this data set in ascending order of the probability predicted by the model.Now divide the data set in bins of some fixed size .More items• | |
C1954 | Non-response bias can be tested by comparing characteristics of respondents who returned completed surveys and non-respondents who failed to return a completed survey. | |
C1955 | The following are the primary advantages of AI:AI drives down the time taken to perform a task. AI enables the execution of hitherto complex tasks without significant cost outlays.AI operates 24x7 without interruption or breaks and has no downtime.AI augments the capabilities of differently abled individuals.More items | |
C1956 | If you know nothing about the data other than the mean, one way to interpret the relative magnitude of the standard deviation is to divide it by the mean. This is called the coefficient of variation. For example, if the mean is 80 and standard deviation is 12, the cv = 12/80 = . 15 or 15%. | |
C1957 | In mathematics, a generating function is a way of encoding an infinite sequence of numbers (an) by treating them as the coefficients of a formal power series. Generating functions are often expressed in closed form (rather than as a series), by some expression involving operations defined for formal series. | |
C1958 | 1:325:14Suggested clip · 104 secondsConditional probability density function - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C1959 | ▶ The endogeneity problem occurs when. ► there is an omitted variable that is correlated with some. regressors. ► the dependent variable and at least one of the independent. variables are determined simultaneously in a system. | |
C1960 | The definition of a dummy dependent variable model is quite simple: If the dependent, response, left-hand side, or Y variable is a dummy variable, you have a dummy dependent variable model. The reason dummy dependent variable models are important is that they are everywhere. | |
C1961 | A marginal distribution is the percentages out of totals, and conditional distribution is the percentages out of some column. Conditional distribution, on the other hand, is the probability distribution of certain values in the table expressed as percentages out of sums (or local totals) of certain rows or columns. | |
C1962 | The purpose of statistical inference is to estimate this sample to sample variation or uncertainty. | |
C1963 | Lasso regression stands for Least Absolute Shrinkage and Selection Operator. The difference between ridge and lasso regression is that it tends to make coefficients to absolute zero as compared to Ridge which never sets the value of coefficient to absolute zero. | |
C1964 | SummaryWeighted Mean: A mean where some values contribute more than others.When the weights add to 1: just multiply each weight by the matching value and sum it all up.Otherwise, multiply each weight w by its matching value x, sum that all up, and divide by the sum of weights: Weighted Mean = ΣwxΣw. | |
C1965 | Back-propagation is the process of calculating the derivatives and gradient descent is the process of descending through the gradient, i.e. adjusting the parameters of the model to go down through the loss function. | |
C1966 | Non-linearity is not a concept specifically in Machine Learning, it is a notion broadly used in mathematics. Linearity means homogeneity of degree 1 and additiveness. This means, given a function , it should be both: homogeneous of degree 1, which means, Additive, which means. | |
C1967 | At Google, we call it Wide & Deep Learning. It's useful for generic large-scale regression and classification problems with sparse inputs (categorical features with a large number of possible feature values), such as recommender systems, search, and ranking problems. | |
C1968 | Pandas have been around for 2 million years Giant pandas have been around a long time. In fact, the first pandas were around over 2 million years ago. That makes them an older species than other bears, like grizzlies, polar bears, and black bears. Giant pandas are only found in the wild in China. | |
C1969 | Assuming the sample size is constant across sampling methods, cluster sampling generally provides less precision than either simple random sampling or stratified sampling. This is the main disadvantage of cluster sampling. | |
C1970 | Artificial intelligence is impacting the future of virtually every industry and every human being. Artificial intelligence has acted as the main driver of emerging technologies like big data, robotics and IoT, and it will continue to act as a technological innovator for the foreseeable future. | |
C1971 | Anything central is in the middle of something — or essential to it. Central things are fundamental and important. Think about the center of a circle: it's right in the middle, equidistant from all sides. Similarly, anything central is in the middle of something. | |
C1972 | To calculate the variance follow these steps:Work out the Mean (the simple average of the numbers)Then for each number: subtract the Mean and square the result (the squared difference).Then work out the average of those squared differences. (Why Square?) | |
C1973 | Research bias, also called experimenter bias, is a process where the scientists performing the research influence the results, in order to portray a certain outcome. | |
C1974 | Classification is the process of classifying the data with the help of class labels. On the other hand, Clustering is similar to classification but there are no predefined class labels. Classification is geared with supervised learning. As against, clustering is also known as unsupervised learning. | |
C1975 | The test statistic used in ANOVA is Student's t. One characteristic of the F distribution is that F cannot be negative. One characteristic of the F distribution is that the computed F can only range between -1 and +1. | |
C1976 | The probability of P(a < Z < b) is calculated as follows. Then express these as their respective probabilities under the standard normal distribution curve: P(Z < b) – P(Z < a) = Φ(b) – Φ(a). Therefore, P(a < Z < b) = Φ(b) – Φ(a), where a and b are positive. | |
C1977 | Confidence intervals measure the degree of uncertainty or certainty in a sampling method. They can take any number of probability limits, with the most common being a 95% or 99% confidence level. Confidence intervals are conducted using statistical methods, such as a t-test. | |
C1978 | The bias is an error from erroneous assumptions in the learning algorithm. High bias can cause an algorithm to miss the relevant relations between features and target outputs. In other words, model with high bias pays very little attention to the training data and oversimplifies the model. | |
C1979 | A certain continuous random variable has a probability density function (PDF) given by: f ( x ) = C x ( 1 − x ) 2 , f(x) = C x (1-x)^2, f(x)=Cx(1−x)2, where x x x can be any number in the real interval [ 0 , 1 ] [0,1] [0,1]. Compute C C C using the normalization condition on PDFs. | |
C1980 | The random walk is simple if Xk = ±1, with P(Xk = 1) = p and P(Xk = −1) = 1−p = q. Imagine a particle performing a random walk on the integer points of the real line, where it in each step moves to one of its neighboring points; see Figure 1. Remark 1. You can also study random walks in higher dimensions. | |
C1981 | This is because of the Fuinjutsu used to seal Kurama. It converts Kurama's chakra into Naruto's passively. This chakra had been changed to adept to Naruto. Similarly when activating the Kurama avatar, the chakra changes to adept to Naruto and takes the form of kyuubi but with the markings of Naruto's seal all over it. | |
C1982 | Data Clustering Basics. The classification of observations into groups requires some methods for computing the distance or the (dis)similarity between each pair of observations. The result of this computation is known as a dissimilarity or distance matrix. | |
C1983 | String interpolation is a process substituting values of variables into placeholders in a string. For instance, if you have a template for saying hello to a person like "Hello {Name of person}, nice to meet you!", you would like to replace the placeholder for name of person with an actual name. | |
C1984 | This is a form of regression, that constrains/ regularizes or shrinks the coefficient estimates towards zero. In other words, this technique discourages learning a more complex or flexible model, so as to avoid the risk of overfitting. A simple relation for linear regression looks like this. | |
C1985 | The importance of data in decision lies in consistency and continual growth. It enables companies to create new business opportunities, generate more revenue, predict future trends, optimize current operational efforts, and produce actionable insights. | |
C1986 | Log loss, aka logistic loss or cross-entropy loss. This is the loss function used in (multinomial) logistic regression and extensions of it such as neural networks, defined as the negative log-likelihood of a logistic model that returns y_pred probabilities for its training data y_true . | |
C1987 | Fallacies of Relevance These fallacies attempt to persuade people with irrelevant information, appealing to emotions rather than logic. Examples of these fallacies include: Appeal to Authority - also referred to as Argumentum ad Verecundia (argument from modesty). | |
C1988 | Quantization, in mathematics and digital signal processing, is the process of mapping input values from a large set (often a continuous set) to output values in a (countable) smaller set, often with a finite number of elements. Rounding and truncation are typical examples of quantization processes. | |
C1989 | Standard deviation is never negative. Standard deviation is sensitive to outliers. A single outlier can raise the standard deviation and in turn, distort the picture of spread. For data with approximately the same mean, the greater the spread, the greater the standard deviation. | |
C1990 | HYPERPLANE. Now that we understand the SVM logic lets formally define the hyperplane . A hyperplane in an n-dimensional Euclidean space is a flat, n-1 dimensional subset of that space that divides the space into two disconnected parts. | |
C1991 | 1:3711:38Suggested clip · 115 secondsFinding the equation of the regression line y on x - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C1992 | Intersection over Union is an evaluation metric used to measure the accuracy of an object detector on a particular dataset. The ground-truth bounding boxes (i.e., the hand labeled bounding boxes from the testing set that specify where in the image our object is). The predicted bounding boxes from our model. | |
C1993 | A Bayesian network (also known as a Bayes network, belief network, or decision network) is a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG). Efficient algorithms can perform inference and learning in Bayesian networks. | |
C1994 | – Validation set: A set of examples used to tune the parameters of a classifier, for example to choose the number of hidden units in a neural network. – Test set: A set of examples used only to assess the performance of a fully-specified classifier. | |
C1995 | 1. It refers to the probability distribution of the robot pose estimate conditioned upon information such as control and sensor measurement data. The extended Kalman filter and particle filter are two different methods for computing the posterior belief. | |
C1996 | If r is not between the positive and negative critical values, then the correlation coefficient is significant. If r is significant, then you may want to use the line for prediction. Suppose you computed r=0.801 using n=10 data points. df=n−2=10−2=8. | |
C1997 | Parametric tests assume a normal distribution of values, or a “bell-shaped curve.” For example, height is roughly a normal distribution in that if you were to graph height from a group of people, one would see a typical bell-shaped curve. | |
C1998 | The XGBoost algorithm is effective for a wide range of regression and classification predictive modeling problems. This modified version of XGBoost is referred to as Class Weighted XGBoost or Cost-Sensitive XGBoost and can offer better performance on binary classification problems with a severe class imbalance. | |
C1999 | Classification/Recognition: Given an image with an object , find out what that object is. In other words, classify it in a class from a set of predefined categories. Localization : Find where the object is and draw a bounding box around it. |
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