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C7200 | A false positive is an outcome where the model incorrectly predicts the positive class. And a false negative is an outcome where the model incorrectly predicts the negative class. In the following sections, we'll look at how to evaluate classification models using metrics derived from these four outcomes. | |
C7201 | Sampling process error occurs because researchers draw different subjects from the same population but still, the subjects have individual differences. Keep in mind that when you take a sample, it is only a subset of the entire population; therefore, there may be a difference between the sample and population. | |
C7202 | 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. | |
C7203 | The main advantage of KNN over other algorithms is that KNN can be used for multiclass classification. Therefore if the data consists of more than two labels or in simple words if you are required to classify the data in more than two categories then KNN can be a suitable algorithm. | |
C7204 | Experimental design is a way to carefully plan experiments in advance so that your results are both objective and valid. The terms “Experimental Design” and “Design of Experiments” are used interchangeably and mean the same thing. | |
C7205 | The sample kurtosis is a useful measure of whether there is a problem with outliers in a data set. Larger kurtosis indicates a more serious outlier problem, and may lead the researcher to choose alternative statistical methods. | |
C7206 | Autocorrelation is diagnosed using a correlogram (ACF plot) and can be tested using the Durbin-Watson test. The auto part of autocorrelation is from the Greek word for self, and autocorrelation means data that is correlated with itself, as opposed to being correlated with some other data. | |
C7207 | Your explanation, for example, could be, “An observation is something you sense: taste, touch, smell, see, or hear. An inference is something you decide or think about a thing or event after you observe it.” | |
C7208 | The next step is known as “Expectation” – step or E-step. In this step, we use the observed data in order to estimate or guess the values of the missing or incomplete data. It is basically used to update the variables. The next step is known as “Maximization”-step or M-step. | |
C7209 | Univariate logistic analysis: When there is one dependent variable, and one independent variable; both are categorical; generally produce Unadjusted model (crude odds ratio) by taking just one independent variable at a time.. Multivariate regression : It's a regression approach of more than one dependent variable. | |
C7210 | In general, solvers return a local minimum (or optimum). A local minimum of a function is a point where the function value is smaller than at nearby points, but possibly greater than at a distant point. A global minimum is a point where the function value is smaller than at all other feasible points. | |
C7211 | Getting to the point, the basic practical difference between Sigmoid and Softmax is that while both give output in [0,1] range, softmax ensures that the sum of outputs along channels (as per specified dimension) is 1 i.e., they are probabilities. Sigmoid just makes output between 0 to 1. | |
C7212 | The p-value is not enough Therefore, a significant p-value tells us that an intervention works, whereas an effect size tells us how much it works. It can be argued that emphasizing the size of effect promotes a more scientific approach, as unlike significance tests, effect size is independent of sample size. | |
C7213 | Stochastic effects have been defined as those for which the probability increases with dose, without a threshold. Nonstochastic effects are those for which incidence and severity depends on dose, but for which there is a threshold dose. These definitions suggest that the two types of effects are not related. | |
C7214 | Steps in selecting a systematic random sample:Calculate the sampling interval (the number of households in the population divided by the number of households needed for the sample)Select a random start between 1 and sampling interval.Repeatedly add sampling interval to select subsequent households. | |
C7215 | You have several options for handling your non normal data. Many tests, including the one sample Z test, T test and ANOVA assume normality. You may still be able to run these tests if your sample size is large enough (usually over 20 items). | |
C7216 | AI and neuroscience researchers agree that current forms of AI cannot have their own emotions, but they can mimic emotion, such as empathy. Synthetic speech also helps reduce the robotic like tone many of these services operate with and emit more realistic emotion. | |
C7217 | The Akaike information criterion (AIC) is an estimator of out-of-sample prediction error and thereby relative quality of statistical models for a given set of data. Given a collection of models for the data, AIC estimates the quality of each model, relative to each of the other models. | |
C7218 | There are two types of sampling methods: Probability sampling involves random selection, allowing you to make statistical inferences about the whole group. Non-probability sampling involves non-random selection based on convenience or other criteria, allowing you to easily collect initial data. | |
C7219 | The Fourier Transform is a mathematical technique that transforms a function of time, x(t), to a function of frequency, X(ω). Making these substitutions in the previous equation yields the analysis equation for the Fourier Transform (also called the Forward Fourier Transform). | |
C7220 | A hidden Markov model (HMM) is a statistical model that can be used to describe the evolution of observable events that depend on internal factors, which are not directly observable. We call the observed event a `symbol' and the invisible factor underlying the observation a `state'. | |
C7221 | Multinomial logistic regression (often just called 'multinomial regression') is used to predict a nominal dependent variable given one or more independent variables. It is sometimes considered an extension of binomial logistic regression to allow for a dependent variable with more than two categories. | |
C7222 | A continuous random variable is normally distributed or has a normal probability distribution if its relative frequency histogram has the shape of a normal curve. We can extend this idea to the shape of other distributions. If μ = 0 and σ = 1, almost all of the data should be between -3 and 3, with the center at 0. | |
C7223 | 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. | |
C7224 | Q-learning is a model-free reinforcement learning algorithm to learn quality of actions telling an agent what action to take under what circumstances. "Q" names the function that the algorithm computes with the maximum expected rewards for an action taken in a given state. | |
C7225 | An RNNs is essentially a fully connected neural network that contains a refactoring of some of its layers into a loop. Among the text usages, the following tasks are among those RNNs perform well at: Sequence labelling. Natural Language Processing (NLP) text classification. | |
C7226 | A probability distribution may be either discrete or continuous. A discrete distribution means that X can assume one of a countable (usually finite) number of values, while a continuous distribution means that X can assume one of an infinite (uncountable) number of different values. | |
C7227 | Fine tuning is one approach to transfer learning. In Transfer Learning or Domain Adaptation we train the model with a dataset and after we train the same model with another dataset that has a different distribution of classes, or even with other classes than in the training dataset). | |
C7228 | Dropout is a regularization technique for neural network models proposed by Srivastava, et al. in their 2014 paper Dropout: A Simple Way to Prevent Neural Networks from Overfitting (download the PDF). Dropout is a technique where randomly selected neurons are ignored during training. They are “dropped-out” randomly. | |
C7229 | In the context of gradient boosting, the training loss is the function that is optimized using gradient descent, e.g., the “gradient” part of gradient boosting models. Specifically, the gradient of the training loss is used to change the target variables for each successive tree. | |
C7230 | Hinge loss is not differentiable and cannot be used with methods which are differentiable like stochastic gradient descent(SGD). In this case Cross entropy(log loss) can be used. | |
C7231 | A Blob is a group of connected pixels in an image that share some common property ( E.g grayscale value ). In the image above, the dark connected regions are blobs, and the goal of blob detection is to identify and mark these regions. | |
C7232 | The level of significance which is selected in Step 1 (e.g., α =0.05) dictates the critical value. For example, in an upper tailed Z test, if α =0.05 then the critical value is Z=1.645. | |
C7233 | It is argued that 'one should not… run away with the concept [of emancipation] to make it all things to all people' (Ayoob 1997: 139). Thus, security is best understood as an 'essentially contested concept' because any sort of fixed definition of security would be unwise; all static definitions have inherent problems. | |
C7234 | The values could be anywhere from, say, 4.5 feet to 7.2 feet. In general, quantities such as pressure, height, mass, weight, density, volume, temperature, and distance are examples of continuous random variables. | |
C7235 | Writing a Questionnaire for a Conjoint Analysis StudyScreener. Explain how to do the first choice question. (Optional) Ask people to explain their first choice. Explain that the following choice questions vary. Count down the number of choice questions. Situational data and cueing. Collect data for validation purposes. Profiling questions.More items• | |
C7236 | Barto (2007), Scholarpedia, 2(11):1604. Temporal difference (TD) learning is an approach to learning how to predict a quantity that depends on future values of a given signal. The name TD derives from its use of changes, or differences, in predictions over successive time steps to drive the learning process. | |
C7237 | a measure of the consistency and freedom from error of a test, as indicated by a correlation coefficient obtained from responses to two or more alternate forms of the test. Also called comparable-forms reliability; equivalent-forms reliability; parallel-forms reliability. | |
C7238 | Binary Search AlgorithmStep 1 - Read the search element from the user.Step 2 - Find the middle element in the sorted list.Step 3 - Compare the search element with the middle element in the sorted list.Step 4 - If both are matched, then display "Given element is found!!!" and terminate the function.More items | |
C7239 | 2 AnswersInspect the topics: Look at the highest-likelihood words in each topic. Do they sound like they form a cohesive "topic" or just some random group of words?Inspect the topic assignments: Hold out a few random documents from training and see what topics LDA assigns to them. | |
C7240 | Tokenization is one of the most common tasks when it comes to working with text data. Tokenization is essentially splitting a phrase, sentence, paragraph, or an entire text document into smaller units, such as individual words or terms. Each of these smaller units are called tokens. | |
C7241 | When talking about kernels in machine learning, most likely the first thing that comes into your mind is the support vector machines (SVM) model because the kernel trick is widely used in the SVM model to bridge linearity and non-linearity. | |
C7242 | There are three main rules associated with basic probability: the addition rule, the multiplication rule, and the complement rule. You can think of the complement rule as the 'subtraction rule' if it helps you to remember it. | |
C7243 | Artificial Intelligence CharacteristicsDeep Learning. Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans, to learn by example. Facial Recognition. Automate Simple and Repetitive Tasks. Data Ingestion. ChatBots. Quantum Computing. Cloud Computing. | |
C7244 | If p is a probability, then p/(1 − p) is the corresponding odds; the logit of the probability is the logarithm of the odds, i.e. For each choice of base, the logit function takes values between negative and positive infinity. | |
C7245 | NumPy is an open-source numerical Python library. NumPy contains a multi-dimensional array and matrix data structures. It can be utilised to perform a number of mathematical operations on arrays such as trigonometric, statistical, and algebraic routines. Pandas objects rely heavily on NumPy objects. | |
C7246 | : varying with something else so as to preserve certain mathematical interrelations. | |
C7247 | According to Markowitz, for every point on the efficient frontier, there is at least one portfolio that can be constructed from all available investments that has the expected risk and return corresponding to that point. The efficient frontier is curved because there is a diminishing marginal return to risk. | |
C7248 | IAT is a popular measure in social psychology to measure the relative strength of association between pairs of concepts (Greenwald, McGhee, & Schwartz, 1998). Studies have found that racial bias IAT studies have a test-retest reliability score of only 0.44, while the IAT overall is just around 0.5. | |
C7249 | Multiply the Total with disease by the Sensitivity to get the number of True positives. Multiply the Total without disease by the Specificity to get the number of True Negatives. Compute the number of False positives and False negatives by subtraction. | |
C7250 | Non-Negative Matrix Factorization (NMF) is an unsupervised technique so there are no labeling of topics that the model will be trained on. The way it works is that, NMF decomposes (or factorizes) high-dimensional vectors into a lower-dimensional representation. | |
C7251 | Image processing is often viewed as arbitrarily manipulating an image to achieve an aesthetic standard or to support a preferred reality. However, image processing is more accurately defined as a means of translation between the human visual system and digital imaging devices. | |
C7252 | Blood Test Helps Determine Patient Response To TNF-Alpha Inhibitors. Baseline levels of serum interferon in rheumatoid arthritis (RA) patients may help rheumatologists determine who may have a poor response to tumour necrosis factor-alpha inhibitor drugs. | |
C7253 | Normal distribution, also known as the Gaussian distribution, is a probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean. In graph form, normal distribution will appear as a bell curve. | |
C7254 | A discrete distribution is one in which the data can only take on certain values, for example integers. A continuous distribution is one in which data can take on any value within a specified range (which may be infinite). | |
C7255 | 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. | |
C7256 | These data points which are way too far from zero will be treated as the outliers. In most of the cases a threshold of 3 or -3 is used i.e., if the Z-score value is greater than or less than 3 or -3 respectively, that data point will be identified as outliers. | |
C7257 | Gaussian random variables and Gaussian random vectors (vectors whose components are jointly Gaussian, as defined later) play a central role in detection and estimation. Jointly Gaussian random variables are completely described by their means and covariances, which is part of the simplicity of working with them. | |
C7258 | The log likelihood This is important because it ensures that the maximum value of the log of the probability occurs at the same point as the original probability function. Therefore we can work with the simpler log-likelihood instead of the original likelihood. | |
C7259 | A regression model that uses L1 regularization technique is called Lasso Regression and model which uses L2 is called Ridge Regression. The key difference between these two is the penalty term. Ridge regression adds “squared magnitude” of coefficient as penalty term to the loss function. | |
C7260 | Sampling is done because you usually cannot gather data from the entire population. Even in relatively small populations, the data may be needed urgently, and including everyone in the population in your data collection may take too long. | |
C7261 | "Controlling" for a variable means adding it to the model so its effect on your outcome variable(s) can be estimated and statistically isolated from the effect of the independent variable you're really interested in. We could also add other variables such as age, education level, and the like. | |
C7262 | 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). | |
C7263 | In positively skewed distributions, the mean is usually greater than the median, which is always greater than the mode. In negatively skewed distributions, the mean is usually less than the median, which is always less than the mode. | |
C7264 | "The function we want to minimize or maximize is called the objective function, or criterion. The loss function computes the error for a single training example, while the cost function is the average of the loss functions of the entire training set. | |
C7265 | Odds ratios measure how many times bigger the odds of one outcome is for one value of an IV, compared to another value. That odds ratio is an unstandardized effect size statistic. It tells you the direction and the strength of the relationship between water temperature and the odds that the plant is present. | |
C7266 | Below are the different regression techniques:Linear Regression.Logistic Regression.Ridge Regression.Lasso Regression.Polynomial Regression.Bayesian Linear Regression. | |
C7267 | Frequency distribution in statistics is a representation that displays the number of observations within a given interval. The representation of a frequency distribution can be graphical or tabular so that it is easier to understand. | |
C7268 | Correlation Test Between Two Variables in RR functions.Import your data into R.Visualize your data using scatter plots.Preleminary test to check the test assumptions.Pearson correlation test. Interpretation of the result. Access to the values returned by cor.test() function.Kendall rank correlation test.Spearman rank correlation coefficient. | |
C7269 | Jakob Bernoulli | |
C7270 | The statistical output displays the coded coefficients, which are the standardized coefficients. Temperature has the standardized coefficient with the largest absolute value. This measure suggests that Temperature is the most important independent variable in the regression model. | |
C7271 | population is the all people or objects to which you wishes to generalize the findings of your study, for instance if your study is about pregnant teenagers , all of the pregnant tens are your target population. Sample frame is a subset of the population and the people or object that you have access to them. | |
C7272 | There are several criteria to be used in evaluating a sorting algorithm:Running time. Typically, an elementary sorting algorithm requires O(N2) steps to sort N randomly arranged items. Memory requirements. The amount of extra memory required by a sorting algorithm is also an important consideration. Stability. | |
C7273 | The median is the middle number in a sorted, ascending or descending, list of numbers and can be more descriptive of that data set than the average. If there is an odd amount of numbers, the median value is the number that is in the middle, with the same amount of numbers below and above. | |
C7274 | What one hot encoding does is, it takes a column which has categorical data, which has been label encoded, and then splits the column into multiple columns. The numbers are replaced by 1s and 0s, depending on which column has what value. So, that's the difference between Label Encoding and One Hot Encoding. | |
C7275 | Calculate precision and recall for all objects present in the image. You also need to consider the confidence score for each object detected by the model in the image. Consider all of the predicted bounding boxes with a confidence score above a certain threshold. | |
C7276 | In statistics, latent variables (from Latin: present participle of lateo (“lie hidden”), as opposed to observable variables) are variables that are not directly observed but are rather inferred (through a mathematical model) from other variables that are observed (directly measured). | |
C7277 | Statistical analysis is used extensively in science, from physics to the social sciences. As well as testing hypotheses, statistics can provide an approximation for an unknown that is difficult or impossible to measure. | |
C7278 | A measure of central location is the single value that best represents a characteristic such as age or height of a group of persons. A measure of dispersion quantifies how much persons in the group vary from each other and from our measure of central location. | |
C7279 | A true positive is an outcome where the model correctly predicts the positive class. Similarly, a true negative is an outcome where the model correctly predicts the negative class. A false positive is an outcome where the model incorrectly predicts the positive class. | |
C7280 | The biggest negative of transfer learning is that it's very hard to do right and very easy to mess up. Especially in NLP this kind of approach has only been mainstream for about a year, which just isn't enough time when model runs take weeks. | |
C7281 | Decomposition is a forecasting technique that separates or decomposes historical data into different components and uses them to create a forecast that is more accurate than a simple trend line. | |
C7282 | (d) PivotTables allow you to filter data, and crosstab queries do not. Crosstab Query and Pivot table are used to get the aggregated data when the data in rows and columns is intersected. Pivot table are modernized then the cross table queries. These tables have filters which can alter the selection criterion. | |
C7283 | In statistics, Markov chain Monte Carlo (MCMC) methods comprise a class of algorithms for sampling from a probability distribution. By constructing a Markov chain that has the desired distribution as its equilibrium distribution, one can obtain a sample of the desired distribution by recording states from the chain. | |
C7284 | The t statistic is the coefficient divided by its standard error. It can be thought of as a measure of the precision with which the regression coefficient is measured. If a coefficient is large compared to its standard error, then it is probably different from 0. | |
C7285 | The rectifier is, as of 2017, the most popular activation function for deep neural networks. A unit employing the rectifier is also called a rectified linear unit (ReLU). | |
C7286 | The definition is: "Entropy is a measure of how evenly energy is distributed in a system. In a physical system, entropy provides a measure of the amount of energy that cannot be used to do work." | |
C7287 | Almost all reinforcement learning algorithms are based on estimating value functions--functions of states (or of state-action pairs) that estimate how good it is for the agent to be in a given state (or how good it is to perform a given action in a given state). The value functions and can be estimated from experience. | |
C7288 | LBPH is one of the easiest face recognition algorithms. It can represent local features in the images. It is possible to get great results (mainly in a controlled environment). It is robust against monotonic gray scale transformations. | |
C7289 | A random variable is a numerical description of the outcome of a statistical experiment. For a discrete random variable, x, the probability distribution is defined by a probability mass function, denoted by f(x). This function provides the probability for each value of the random variable. | |
C7290 | Sometimes known as the secret to a good horror or drama, non-linear sounds are sounds that are too loud for the normal musical range of an instrument or an animal's vocal chords. One contained emotionally neutral film scores and the other contained nonlinear sounds. | |
C7291 | Constrained optimization problems are problems for which a function is to be minimized or maximized subject to constraints . Here is called the objective function and is a Boolean-valued formula. stands for "maximize subject to constraints ". You say a point satisfies the constraints if is true. | |
C7292 | - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C7293 | Definition: Two events, A and B, are independent if the fact that A occurs does not affect the probability of B occurring. Some other examples of independent events are: Landing on heads after tossing a coin AND rolling a 5 on a single 6-sided die. Choosing a marble from a jar AND landing on heads after tossing a coin. | |
C7294 | It is common to allocate 50 percent or more of the data to the training set, 25 percent to the test set, and the remainder to the validation set. Some training sets may contain only a few hundred observations; others may include millions. | |
C7295 | This is because geometric mean involves product term. However, for a data which follows log-normal distribution, geometric mean should be same as median. | |
C7296 | The 7 Steps of Machine Learning1 - Data Collection.2 - Data Preparation.3 - Choose a Model.4 - Train the Model.5 - Evaluate the Model.6 - Parameter Tuning.7 - Make Predictions.More items | |
C7297 | Importance sampling is a variance reduction technique that can be used in the Monte Carlo method. The idea behind importance sampling is that certain values of the input random variables in a simulation have more impact on the parameter being estimated than others. | |
C7298 | To explain eigenvalues, we first explain eigenvectors. Almost all vectors change di- rection, when they are multiplied by A. Certain exceptional vectors x are in the same direction as Ax. Those are the “eigenvectors”. Multiply an eigenvector by A, and the vector Ax is a number times the original x. | |
C7299 | While e-learning won't replace traditional classrooms, it will change the way we know them today. With improved resources and reduced teacher workloads, classrooms can shift to co-learning spaces. Students can arrive, learn, engage—all at their own pace in a collaborative environment. |
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