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C7300 | Kurtosis is a measure of whether the data are heavy-tailed or light-tailed relative to a normal distribution. That is, data sets with high kurtosis tend to have heavy tails, or outliers. Data sets with low kurtosis tend to have light tails, or lack of outliers. A uniform distribution would be the extreme case. | |
C7301 | 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. | |
C7302 | Before you run any statistical test, you must first determine your alpha level, which is also called the “significance level.” By definition, the alpha level is the probability of rejecting the null hypothesis when the null hypothesis is true. Like all probabilities, alpha ranges from 0 to 1. | |
C7303 | Bias is the simplifying assumptions made by the model to make the target function easier to approximate. Variance is the amount that the estimate of the target function will change given different training data. Trade-off is tension between the error introduced by the bias and the variance. | |
C7304 | A population regression function is a linear function, which hypothesizes a theoretical relationship between a dependent variable and a set of independent or explanatory variables at a population level. A stochastic error terms is present in the regression model as well. | |
C7305 | No, because clustering and classification (or supervised learning) are two different philosophies of machine learning. Alternatively, if you don't have class labels, you can't do classification and only clustering is possible to understand the possible groups within the data. | |
C7306 | An example of an argument that fits the form modus ponens: If today is Tuesday, then John will go to work. An argument can be valid but nonetheless unsound if one or more premises are false; if an argument is valid and all the premises are true, then the argument is sound. | |
C7307 | • Model capacity is ability to fit variety of functions. – Model with Low capacity struggles to fit training set. – A High capacity model can overfit by memorizing. properties of training set not useful on test set. • When model has higher capacity, it overfits. | |
C7308 | Criteria for CausalityStrength: A relationship is more likely to be causal if the correlation coefficient is large and statistically significant.Consistency: A relationship is more likely to be causal if it can be replicated.More items• | |
C7309 | The Correlation Coefficient When the r value is closer to +1 or -1, it indicates that there is a stronger linear relationship between the two variables. A correlation of -0.97 is a strong negative correlation while a correlation of 0.10 would be a weak positive correlation. | |
C7310 | Thus, Linear regression is better for simpler modelling while neural net is better for complex or multiple-level/category modelling. Neural networks generally outperform linear regression as they have more degrees of freedom. In linear regression variables are treated as a linear combination. | |
C7311 | standard normal distribution | |
C7312 | The term general linear model (GLM) usually refers to conventional linear regression models for a continuous response variable given continuous and/or categorical predictors. It includes multiple linear regression, as well as ANOVA and ANCOVA (with fixed effects only). | |
C7313 | 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). | |
C7314 | Quota sampling means to take a very tailored sample that's in proportion to some characteristic or trait of a population. For example, if your population consists of 45% female and 55% male, your sample should reflect those percentages. | |
C7315 | Wrapper methods measure the “usefulness” of features based on the classifier performance. In contrast, the filter methods pick up the intrinsic properties of the features (i.e., the “relevance” of the features) measured via univariate statistics instead of cross-validation performance. | |
C7316 | Machine learning algorithms are able to improve without being explicitly programmed. In other words, they are able to find patterns in the data and apply those patterns to new challenges in the future. Deep learning is a subset of machine learning, which uses neural networks with many layers. | |
C7317 | How To Overcome Confirmation Bias And Expand Your MindDon't Be Afraid. Know That Your Ego Doesn't Want You To Expand Your Mind. Think For Yourself. If You Want To Expand Your Mind, You Must Be OK With Disagreements. Ask Good Questions. Keep Information Channels Open. | |
C7318 | The formula for calculating lambda is: Lambda = (E1 – E2) / E1. Lambda may range in value from 0.0 to 1.0. Zero indicates that there is nothing to be gained by using the independent variable to predict the dependent variable. | |
C7319 | The difference between the two norms is that the standard deviation is calculating the square of the difference whereas the mean absolute deviation is only looking at the absolute difference. Hence large outliers will create a higher dispersion when using the standard deviation instead of the other method. | |
C7320 | If you are a beginner, I can recommend you as below.Quickly learn Python first.Take a course of AI and Machine learning (several online courses are there). You can try MIT OCW also.Then start with Tutorial of TensorFlow website (https://www.tensorflow.org/versions/0.6.0/tutorials/index.html ) | |
C7321 | The amount that the weights are updated during training is referred to as the step size or the “learning rate.” 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. | |
C7322 | In machine learning, model validation is referred to as the process where a trained model is evaluated with a testing data set. The testing data set is a separate portion of the same data set from which the training set is derived. | |
C7323 | This post is about various evaluation metrics and how and when to use them.Accuracy, Precision, and Recall: A. F1 Score: This is my favorite evaluation metric and I tend to use this a lot in my classification projects. Log Loss/Binary Crossentropy. Categorical Crossentropy. AUC. | |
C7324 | TensorBoard is a tool for providing the measurements and visualizations needed during the machine learning workflow. It enables tracking experiment metrics like loss and accuracy, visualizing the model graph, projecting embeddings to a lower dimensional space, and much more. | |
C7325 | Contrapositive: The contrapositive of a conditional statement of the form "If p then q" is "If ~q then ~p". Symbolically, the contrapositive of p q is ~q ~p. A conditional statement is logically equivalent to its contrapositive. | |
C7326 | The converse of the conditional statement is “If Q then P.” The contrapositive of the conditional statement is “If not Q then not P.” The inverse of the conditional statement is “If not P then not Q.” | |
C7327 | Mathwords: Contrapositive. Switching the hypothesis and conclusion of a conditional statement and negating both. For example, the contrapositive of "If it is raining then the grass is wet" is "If the grass is not wet then it is not raining." | |
C7328 | The F-distribution is either zero or positive, so there are no negative values for F. This feature of the F-distribution is similar to the chi-square distribution. The F-distribution is skewed to the right. Thus this probability distribution is nonsymmetrical. | |
C7329 | 1 . Two main measures for the efficiency of an algorithm areProcessor and memory.Complexity and capacity.Time and space.Data and space. | |
C7330 | Bias is calculated as the product of two components: non-response rate and the difference between the observed and non-respondent answers. Increasing either of the two components will lead to an increase in bias. | |
C7331 | The least squares regression line is the line that best fits the data. Its slope and y-intercept are computed from the data using formulas. The sum of the squared errors SSE of the least squares regression line can be computed using a formula, without having to compute all the individual errors. | |
C7332 | A null hypothesis is a type of hypothesis used in statistics that proposes that there is no difference between certain characteristics of a population (or data-generating process). For example, a gambler may be interested in whether a game of chance is fair. | |
C7333 | In statistics, imputation is the process of replacing missing data with substituted values. That is to say, when one or more values are missing for a case, most statistical packages default to discarding any case that has a missing value, which may introduce bias or affect the representativeness of the results. | |
C7334 | A Z-score is a numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean. A Z-Score is a statistical measurement of a score's relationship to the mean in a group of scores. | |
C7335 | Techniques of Forecasting: Simple Moving Average (SMA) Exponential Smoothing (SES) Autoregressive Integration Moving Average (ARIMA) Neural Network (NN) | |
C7336 | Noisy data can appear as normal data. So noise objects are not always outliers. | |
C7337 | Generally, a value of r greater than 0.7 is considered a strong correlation. Anything between 0.5 and 0.7 is a moderate correlation, and anything less than 0.4 is considered a weak or no correlation. | |
C7338 | The P-value is the probability that a chi-square statistic having 2 degrees of freedom is more extreme than 19.58. We use the Chi-Square Distribution Calculator to find P(Χ2 > 19.58) = 0.0001. Interpret results. Since the P-value (0.0001) is less than the significance level (0.05), we cannot accept the null hypothesis. | |
C7339 | The main difference between cluster sampling and stratified sampling is that in cluster sampling the cluster is treated as the sampling unit so sampling is done on a population of clusters (at least in the first stage). In stratified sampling, the sampling is done on elements within each stratum. | |
C7340 | How do you calculate precision and recall for multiclass classification using confusion matrix?Precision = TP / (TP+FP)Recall = TP / (TP+FN) | |
C7341 | Deep Learning is the evolution of Machine Learning and it will definitely help in making machines better than what Machine Learning does. But one thing to note is that Deep Learning models require a very large amount of data to train the model otherwise it won't work as expected. | |
C7342 | To find percent agreement for two raters, a table (like the one above) is helpful.Count the number of ratings in agreement. In the above table, that's 3.Count the total number of ratings. For this example, that's 5.Divide the total by the number in agreement to get a fraction: 3/5.Convert to a percentage: 3/5 = 60%. | |
C7343 | Relu : More computationally efficient to compute than Sigmoid like functions since Relu just needs to pick max(0, x) and not perform expensive exponential operations as in Sigmoids. Relu : In practice, networks with Relu tend to show better convergence performance than sigmoid. | |
C7344 | The problem is to find the probability of landing at a given spot after a given number of steps, and, in particular, to find how far away you are on average from where you started. Why do we care about this game? The random walk is central to statistical physics. | |
C7345 | The set of all the possible outcomes is called the sample space of the experiment and is usually denoted by S. Any subset E of the sample space S is called an event. E = {2,4,6} is an event, which can be described in words as ”the number is even”. Example 3 Tossing a coin twice. | |
C7346 | Regression and classification are categorized under the same umbrella of supervised machine learning. The main difference between them is that the output variable in regression is numerical (or continuous) while that for classification is categorical (or discrete). | |
C7347 | The mean is an important measure because it incorporates the score from every subject in the research study. The required steps for its calculation are: count the total number of cases—referred in statistics as n; add up all the scores and divide by the total number of cases. | |
C7348 | AB testing is essentially an experiment where two or more variants of a page are shown to users at random, and statistical analysis is used to determine which variation performs better for a given conversion goal. | |
C7349 | Recall quantifies the number of positive class predictions made out of all positive examples in the dataset. F-Measure provides a single score that balances both the concerns of precision and recall in one number. | |
C7350 | Pattern recognition requires repetition of experience. Semantic memory, which is used implicitly and subconsciously is the main type of memory involved with recognition. The development of neural networks in the outer layer of the brain in humans has allowed for better processing of visual and auditory patterns. | |
C7351 | A random variable, usually written X, is a variable whose possible values are numerical outcomes of a random phenomenon. There are two types of random variables, discrete and continuous. | |
C7352 | AI or artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning, reasoning and self-correction. Some of the applications of AI include expert systems, speech recognition and machine vision. | |
C7353 | AI works by combining large amounts of data with fast, iterative processing and intelligent algorithms, allowing the software to learn automatically from patterns or features in the data. Cognitive computing is a subfield of AI that strives for a natural, human-like interaction with machines. | |
C7354 | 3:1615:06Suggested clip · 98 secondsOrdinal logistic regression using SPSS (July, 2019) - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C7355 | Deep learning is a subset of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data. Deep learning allows machines to solve complex problems even when using a data set that is very diverse, unstructured and inter-connected. | |
C7356 | Within an artificial neural network, a neuron is a mathematical function that model the functioning of a biological neuron. Typically, a neuron compute the weighted average of its input, and this sum is passed through a nonlinear function, often called activation function, such as the sigmoid. | |
C7357 | A sample is a randomly chosen selection of elements from an underlying population. Sample covariance measures the strength and the direction of the relationship between the elements of two samples, and the sample correlation is derived from the covariance. | |
C7358 | 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. | |
C7359 | Hyperbolic Tangent (Sigmoid) Kernel The Sigmoid Kernel comes from the Neural Networks field, where the bipolar sigmoid function is often used as an activation function for artificial neurons. This kernel was quite popular for support vector machines due to its origin from neural network theory. | |
C7360 | The Top 5 Uses of Image Recognition#1. Automated Image Organization – from Cloud Apps to Telecoms.#2. Stock Photography and Video Websites.#3. Visual Search for Improved Product Discoverability.#4. Image Classification for Websites with Large Visual Databases.#5. #6. Celebrating the Power of Image Recognition. | |
C7361 | Such algorithms are called greedy because while the optimal solution to each smaller instance will provide an immediate output, the algorithm doesn't consider the larger problem as a whole. Greedy algorithms work by recursively constructing a set of objects from the smallest possible constituent parts. | |
C7362 | Gaussian Noise is a statistical noise having a probability density function equal to normal distribution, also known as Gaussian Distribution. Random Gaussian function is added to Image function to generate this noise. It is also called as electronic noise because it arises in amplifiers or detectors. | |
C7363 | In mathematics, a divergent series is an infinite series that is not convergent, meaning that the infinite sequence of the partial sums of the series does not have a finite limit. If a series converges, the individual terms of the series must approach zero. | |
C7364 | A simple random sample is a subset of a statistical population in which each member of the subset has an equal probability of being chosen. A simple random sample is meant to be an unbiased representation of a group. Random sampling is used in science to conduct randomized control tests or for blinded experiments. | |
C7365 | A mode of a continuous probability distribution is often considered to be any value x at which its probability density function has a locally maximum value, so any peak is a mode. In symmetric unimodal distributions, such as the normal distribution, the mean (if defined), median and mode all coincide. | |
C7366 | k-means clustering is a method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean (cluster centers or cluster centroid), serving as a prototype of the cluster. | |
C7367 | It basically defined on probability estimates and measures the performance of a classification model where the input is a probability value between 0 and 1. It can be understood more clearly by differentiating it with accuracy. | |
C7368 | They are used for different purposes. Gradient descent, in its vanilla form, minimizes an unconstrained optimization problem. To handle constraints, you can use some modifications like projected gradient descent. | |
C7369 | The mass density (ρ) of a substance is the mass of one unit volume of the substance. The relative density is the ratio of the mass of the substance in air at 20 °C to that of an equal volume of water at the same temperature. | |
C7370 | Research has shown that the Wechsler test is one of the most well-designed tests to measure intelligence. However, as most tests of this nature are, the tests are only as reliable as the person giving them. | |
C7371 | (Example: a test with 90% specificity will correctly return a negative result for 90% of people who don't have the disease, but will return a positive result — a false-positive — for 10% of the people who don't have the disease and should have tested negative.) | |
C7372 | A manifest variable is a variable or factor that can be directly measured or observed. It is the opposite of a latent variable, which is a factor that cannot be directly observed, and which needs a manifest variable assigned to it as an indicator to test whether it is present. | |
C7373 | Probability is about a finite set of possible outcomes, given a probability. Likelihood is about an infinite set of possible probabilities, given an outcome. | |
C7374 | The fact is almost all big data sets, generated by systems powered by ML/AI based models, are known to be biased. However, most ML modelers are not aware of these biases and even if they are, they do not know what to do about it. Most (almost all) big datasets generated by ML powered systems are biased. | |
C7375 | Essentially, multivariate analysis is a tool to find patterns and relationships between several variables simultaneously. It lets us predict the effect a change in one variable will have on other variables. Multivariate analysis is also highly graphical in its approach. | |
C7376 | Neural network activation functions are a crucial component of deep learning. Activation functions determine the output of a deep learning model, its accuracy, and also the computational efficiency of training a model—which can make or break a large scale neural network. | |
C7377 | Message queues enable asynchronous communication, which means that the endpoints that are producing and consuming messages interact with the queue, not each other. Producers can add requests to the queue without waiting for them to be processed. Consumers process messages only when they are available. | |
C7378 | 5 Techniques to Prevent Overfitting in Neural NetworksSimplifying The Model. The first step when dealing with overfitting is to decrease the complexity of the model. Early Stopping. Early stopping is a form of regularization while training a model with an iterative method, such as gradient descent. Use Data Augmentation. Use Regularization. Use Dropouts. | |
C7379 | We will learn Classification algorithms, types of classification algorithms, support vector machines(SVM), Naive Bayes, Decision Tree and Random Forest Classifier in this tutorial. | |
C7380 | The short answer is yes—because most regression models will not perfectly fit the data at hand. If you need a more complex model, applying a neural network to the problem can provide much more prediction power compared to a traditional regression. | |
C7381 | Optimizers are algorithms or methods used to change the attributes of your neural network such as weights and learning rate in order to reduce the losses. How you should change your weights or learning rates of your neural network to reduce the losses is defined by the optimizers you use. | |
C7382 | Entropy is the measure of disorder in a thermodynamic system.Difference Between Enthalpy and EntropyEnthalpy is a kind of energyEntropy is a propertyIt is the sum of internal energy and flows energyIt is the measurement of the randomness of moleculesIt is denoted by symbol HIt is denoted by symbol S5 more rows | |
C7383 | The purpose and importance of the null hypothesis and alternative hypothesis are that they provide an approximate description of the phenomena. The purpose is to provide the researcher or an investigator with a relational statement that is directly tested in a research study. | |
C7384 | The role of a fully connected layer in a CNN architecture The objective of a fully connected layer is to take the results of the convolution/pooling process and use them to classify the image into a label (in a simple classification example). | |
C7385 | A sampling frame is a list or other device used to define a researcher's population of interest. The sampling frame defines a set of elements from which a researcher can select a sample of the target population. | |
C7386 | Some additional simple scoring methods include:Counts. Count the number of times each word appears in a document.Frequencies. Calculate the frequency that each word appears in a document out of all the words in the document. | |
C7387 | Similar to supervised learning, a neural network can be used in a way to train on unlabeled data sets. This type of algorithms are categorized under unsupervised learning algorithms and are useful in a multitude of tasks such as clustering. | |
C7388 | Nearly 150 years ago, Charles Darwin proposed that morality was a byproduct of evolution, a human trait that arose as natural selection shaped man into a highly social species—and the capacity for morality, he argued, lay in small, subtle differences between us and our closest animal relatives. | |
C7389 | 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. | |
C7390 | In probability theory and statistics, the moment-generating function of a real-valued random variable is an alternative specification of its probability distribution. There are particularly simple results for the moment-generating functions of distributions defined by the weighted sums of random variables. | |
C7391 | Markov chains are an important concept in stochastic processes. They can be used to greatly simplify processes that satisfy the Markov property, namely that the future state of a stochastic variable is only dependent on its present state. | |
C7392 | Cautious and uncertain, AI systems will seek additional information and learn to navigate the confusing situations they encounter. Of course, self-driving cars shouldn't have to ask questions. If a car's image detection spots a foreign object up ahead, for instance, it won't have time to ask humans for help. | |
C7393 | Parametric statistics generally require interval or ratio data. An example of this type of data is age, income, height, and weight in which the values are continuous and the intervals between values have meaning. In contrast, nonparametric statistics are typically used on data that nominal or ordinal. | |
C7394 | Critic Loss: D(x) - D(G(z)) The discriminator tries to maximize this function. In other words, it tries to maximize the difference between its output on real instances and its output on fake instances. | |
C7395 | Hyperparameters are the variables which determines the network structure(Eg: Number of Hidden Units) and the variables which determine how the network is trained(Eg: Learning Rate). Hyperparameters are set before training(before optimizing the weights and bias). | |
C7396 | The median filter is a non-linear digital filtering technique, often used to remove noise from an image or signal. Such noise reduction is a typical pre-processing step to improve the results of later processing (for example, edge detection on an image). | |
C7397 | ROC curves in logistic regression are used for determining the best cutoff value for predicting whether a new observation is a "failure" (0) or a "success" (1). Your observed outcome in logistic regression can ONLY be 0 or 1. The predicted probabilities from the model can take on all possible values between 0 and 1. | |
C7398 | Cross-entropy is a measure of the difference between two probability distributions for a given random variable or set of events. You might recall that information quantifies the number of bits required to encode and transmit an event. | |
C7399 | A and B are mutually exclusive events if they cannot occur at the same time. This means that A and B do not share any outcomes and P(A AND B) = 0. For example, suppose the sample space S = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}. |
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