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C2800 | The degrees of freedom for the chi-square are calculated using the following formula: df = (r-1)(c-1) where r is the number of rows and c is the number of columns. If the observed chi-square test statistic is greater than the critical value, the null hypothesis can be rejected. | |
C2801 | Testing approach: The answers lie in the data set. In order to test a machine learning algorithm, tester defines three different datasets viz. Training dataset, validation dataset and a test dataset (a subset of training dataset). | |
C2802 | The standard error of estimate, Se indicates approximately how much error you make when you use the predicted value for Y (on the least-squares line) instead of the actual value of Y. | |
C2803 | Basically, you're just pre-setting some of the weights of the new network. Be sure to initialize the new connections to have similar distributions. Make the last layer a concatenation of their results and then add another few layers. Make the last layer a concatenation of their results and the original input. | |
C2804 | They are basically equivalent: the linear time invariant systems refers to an analog system and shift-invariant system refers to a discrete-time system. The shift-invariant is the same as time invariant: if we delay the input, the output that we get is the original input to the signal that wasn't delayed. | |
C2805 | 1 Answer. Transfer learning is when a model developed for one task is reused to work on a second task. Fine tuning is one approach to transfer learning. | |
C2806 | A feedforward neural network is a biologically inspired classification algorithm. It consist of a (possibly large) number of simple neuron-like processing units, organized in layers. Every unit in a layer is connected with all the units in the previous layer. This is why they are called feedforward neural networks. | |
C2807 | Pruning reduces the size of decision trees by removing parts of the tree that do not provide power to classify instances. Decision trees are the most susceptible out of all the machine learning algorithms to overfitting and effective pruning can reduce this likelihood. | |
C2808 | In the case of information retrieval, the cosine similarity of two documents will range from 0 to 1, since the term frequencies (using tf–idf weights) cannot be negative. The angle between two term frequency vectors cannot be greater than 90°. | |
C2809 | Cohen's kappa coefficient (κ) is a statistic that is used to measure inter-rater reliability (and also Intra-rater reliability) for qualitative (categorical) items. | |
C2810 | The algorithm is used to effectively train a neural network through a method called chain rule. In simple terms, after each forward pass through a network, backpropagation performs a backward pass while adjusting the model's parameters (weights and biases). | |
C2811 | Because learning grammatical regularities requires infants to be able to determine boundaries between individual words, this indicates that infants who are still quite young are able to acquire multiple levels of language knowledge (both lexical and syntactical) simultaneously, indicating that statistical learning is a | |
C2812 | A relative frequency distribution shows the proportion of the total number of observations associated with each value or class of values and is related to a probability distribution, which is extensively used in statistics. | |
C2813 | To implement stratified sampling, first find the total number of members in the population, and then the number of members of each stratum. For each stratum, divide the number of members by the total number in the entire population to get the percentage of the population represented by that stratum. | |
C2814 | The correlation between two true dichotomous variables is called a phi coefficient. This can be computed either by just obtaining the Pearson's r between your X and Y variables (each of them with scores of 1 and 0, or for that matter, any two numbers that differ). | |
C2815 | Stratified random sampling refers to a sampling method that has the following properties.The population consists of N elements.The population is divided into H groups, called strata.Each element of the population can be assigned to one, and only one, stratum.More items | |
C2816 | All descriptive statistics are either measures of central tendency or measures of variability, also known as measures of dispersion. Range, quartiles, absolute deviation and variance are all examples of measures of variability. Consider the following data set: 5, 19, 24, 62, 91, 100. | |
C2817 | Anomaly detection (aka outlier analysis) is a step in data mining that identifies data points, events, and/or observations that deviate from a dataset's normal behavior. Anomalous data can indicate critical incidents, such as a technical glitch, or potential opportunities, for instance a change in consumer behavior. | |
C2818 | If the dice is thrown repeatedly until the first time a three appears. The probablility distribution of the number of times it is thrown not getting a three (not-a-threes number of failures to get a three) is a geometric distribution with the success_fraction = 1/6 = 0.1666 ̇. | |
C2819 | For example, The number of cases of a disease in different towns; The number of mutations in given regions of a chromosome; The number of dolphin pod sightings along a flight path through a region; The number of particles emitted by a radioactive source in a given time; The number of births per hour during a given day. | |
C2820 | Regression. Regression analysis attempts to determine the best "fit" between two or more variables. The independent variable in a regression analysis is a continuous variable, and thus allows you to determine how one or more independent variables predict the values of a dependent variable. | |
C2821 | For example, if n = 100 and p = 0.25 then we are justified in using the normal approximation. This is because np = 25 and n(1 - p) = 75. Since both of these numbers are greater than 10, the appropriate normal distribution will do a fairly good job of estimating binomial probabilities. | |
C2822 | Using batch normalisation allows much higher learning rates, increasing the speed at which networks train. Makes weights easier to initialise — Weight initialisation can be difficult, especially when creating deeper networks. Batch normalisation helps reduce the sensitivity to the initial starting weights. | |
C2823 | Intelligence Quotient | |
C2824 | There are many moving parts in a Machine Learning (ML) model that have to be tied together for an ML model to execute and produce results successfully. This process of tying together different pieces of the ML process is known as a pipeline. A pipeline is a generalized but very important concept for a Data Scientist. | |
C2825 | Backpropagation, short for "backward propagation of errors," is an algorithm for supervised learning of artificial neural networks using gradient descent. Given an artificial neural network and an error function, the method calculates the gradient of the error function with respect to the neural network's weights. | |
C2826 | A random variable is a variable whose value is unknown or a function that assigns values to each of an experiment's outcomes. | |
C2827 | Systematic vs. Random errors are (like the name suggests) completely random. They are unpredictable and can't be replicated by repeating the experiment again. Systematic Errors produce consistent errors, either a fixed amount (like 1 lb) or a proportion (like 105% of the true value). | |
C2828 | Answer to Try It! Variables that give a straight line with a constant slope are said to have a linear relationship. In this case, however, the relationship is nonlinear. The slope changes all along the curve. | |
C2829 | In general, there is no universal rule of thumb indicating that the accuracy of a learner is directly proportional to the number of features used to train it. | |
C2830 | Time series analysis can be useful to see how a given asset, security, or economic variable changes over time. It can also be used to examine how the changes associated with the chosen data point compare to shifts in other variables over the same time period. | |
C2831 | The bias is the value of the difference between two techniques (reading A – reading B) and this value is plotted on the y axis, against the mean of the two techniques (reading A+reading B/2) on the x‐axis) (Fig. 1). If the two methods you are comparing give very similar results, your bias should be very close to zero. | |
C2832 | This cheat sheet demonstrates 11 different classical time series forecasting methods; they are:Autoregression (AR)Moving Average (MA)Autoregressive Moving Average (ARMA)Autoregressive Integrated Moving Average (ARIMA)Seasonal Autoregressive Integrated Moving-Average (SARIMA)More items• | |
C2833 | As in classification, support vector regression (SVR) is characterized by the use of kernels, sparse solution, and VC control of the margin and the number of support vectors. Although less popular than SVM, SVR has been proven to be an effective tool in real-value function estimation. | |
C2834 | 1:246:12Suggested clip · 104 secondsBuilding Statistical Models - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C2835 | Deep learning models are trained by using large sets of labeled data and neural network architectures that learn features directly from the data without the need for manual feature extraction. Figure 1: Neural networks, which are organized in layers consisting of a set of interconnected nodes. | |
C2836 | Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving. | |
C2837 | A neural network is considered to be an effort to mimic human brain actions in a simplified manner. Attention Mechanism is also an attempt to implement the same action of selectively concentrating on a few relevant things, while ignoring others in deep neural networks. | |
C2838 | A P value is also affected by sample size and the magnitude of effect. Generally the larger the sample size, the more likely a study will find a significant relationship if one exists. As the sample size increases the impact of random error is reduced. | |
C2839 | 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. | |
C2840 | The sample variance is an estimator for the population variance. When applied to sample data, the population variance formula is a biased estimator of the population variance: it tends to underestimate the amount of variability. We are using one fitted value (sample mean) in our estimate of the variance. | |
C2841 | Decision trees can handle both categorical and numerical variables at the same time as features, there is not any problem in doing that. Edit: Every split in a decision tree is based on a feature. If the feature is categorical, the split is done with the elements belonging to a particular class. | |
C2842 | Every box is composed of four parts (or areas), defined by their respective edges: the content edge, padding edge, border edge, and margin edge. | |
C2843 | Structural equation modeling is a multivariate statistical analysis technique that is used to analyze structural relationships. This technique is the combination of factor analysis and multiple regression analysis, and it is used to analyze the structural relationship between measured variables and latent constructs. | |
C2844 | To quickly summarize: Image Classification helps us to classify what is contained in an image. Image Localization will specify the location of single object in an image whereas Object Detection specifies the location of multiple objects in the image. | |
C2845 | The concept of exclusion restrictions denotes that some of the exogenous variables are not in some of the equations. Often this idea is expressed by saying the coefficient next to that exogenous variable is zero. | |
C2846 | Multinomial theorem is actually not in the syllabus but learning this topic well can save your time as well as will make the problems of permutations and combinations look easy. | |
C2847 | AUC - ROC curve is a performance measurement for classification problem at various thresholds settings. ROC is a probability curve and AUC represents degree or measure of separability. By analogy, Higher the AUC, better the model is at distinguishing between patients with disease and no disease. | |
C2848 | SVM is a supervised machine learning algorithm which can be used for classification or regression problems. It uses a technique called the kernel trick to transform your data and then based on these transformations it finds an optimal boundary between the possible outputs. | |
C2849 | Autocorrelation (for sound signals)(1) finding the value of the signal at a time t,(2) finding the value of the signal at a time t + τ,(3) multiplying those two values together,(4) repeating the process for all possible times, t, and then.(5) computing the average of all those products. | |
C2850 | The linear Discriminant analysis estimates the probability that a new set of inputs belongs to every class. LDA uses Bayes' Theorem to estimate the probabilities. If the output class is (k) and the input is (x), here is how Bayes' theorem works to estimate the probability that the data belongs to each class. | |
C2851 | Machine learning is a form of artificial intelligence (AI) that teaches computers to think in a similar way to how humans do: learning and improving upon past experiences. It works by exploring data, identifying patterns, and involves minimal human intervention. | |
C2852 | Attention Mechanism in Neural Networks - 1. Attention is arguably one of the most powerful concepts in the deep learning field nowadays. It is based on a common-sensical intuition that we “attend to” a certain part when processing a large amount of information. | |
C2853 | SVM is a supervised machine learning algorithm which can be used for classification or regression problems. It uses a technique called the kernel trick to transform your data and then based on these transformations it finds an optimal boundary between the possible outputs. | |
C2854 | Simply head on over to www.canva.com to start creating your decision tree design. You don't need to download Canva, just create an account and log in. | |
C2855 | Boltzmann machine is an unsupervised machine learning algorithm. It helps discover latent features present in the dataset. Dataset is composed of binary vectors. Connection between nodes are undirected. | |
C2856 | A greedy algorithm is used to construct a Huffman tree during Huffman coding where it finds an optimal solution. In decision tree learning, greedy algorithms are commonly used, however they are not guaranteed to find the optimal solution. One popular such algorithm is the ID3 algorithm for decision tree construction. | |
C2857 | To do so, you can completely opt out from the automatic change detection in your component, and handle things yourself, by injecting in your component a ChangeDetectorRef . This class offers a few methods: detach() detectChanges() | |
C2858 | Here is a brief review of our original seven techniques for dimensionality reduction:Missing Values Ratio. Low Variance Filter. High Correlation Filter. Random Forests/Ensemble Trees. Principal Component Analysis (PCA). Backward Feature Elimination. Forward Feature Construction. | |
C2859 | A recurrent neural network (RNN) is a type of artificial neural network commonly used in speech recognition and natural language processing (NLP). RNNs are designed to recognize a data's sequential characteristics and use patterns to predict the next likely scenario. | |
C2860 | formal parameter — the identifier used in a method to stand for the value that is passed into the method by a caller. actual parameter — the actual value that is passed into the method by a caller. | |
C2861 | To recap the differences between the two: Machine learning uses algorithms to parse data, learn from that data, and make informed decisions based on what it has learned. Deep learning structures algorithms in layers to create an "artificial neural network” that can learn and make intelligent decisions on its own. | |
C2862 | In mathematical optimization and decision theory, a loss function or cost function is a function that maps an event or values of one or more variables onto a real number intuitively representing some "cost" associated with the event. In optimal control, the loss is the penalty for failing to achieve a desired value. | |
C2863 | Binning is a way to group a number of more or less continuous values into a smaller number of "bins". For example, if you have data about a group of people, you might want to arrange their ages into a smaller number of age intervals. | |
C2864 | A (non-mathematical) definition I like by Miller (2017)3 is: Interpretability is the degree to which a human can understand the cause of a decision. The higher the interpretability of a machine learning model, the easier it is for someone to comprehend why certain decisions or predictions have been made. | |
C2865 | In probability theory and statistics, a collection of random variables is independent and identically distributed if each random variable has the same probability distribution as the others and all are mutually independent. This property is usually abbreviated as i.i.d. or iid or IID. | |
C2866 | AI is not one technology; it's a set of technologies and building blocks, all using data to unlock intelligent value across industries and business functions. AI Consulting Services from IBM help you leverage AI to drive smart reinvention of your workflows and technology. | |
C2867 | Basically, we can think of TensorFlow as the Lego bricks (similar to NumPy and SciPy) that we can use to implement machine learning algorithms whereas Scikit-Learn comes with off-the-shelf algorithms, e.g., algorithms for classification such as SVMs, Random Forests, Logistic Regression, and many, many more. | |
C2868 | With a continuous variable, the hazard ratio indicates the change in the risk of death if the parameter in question rises by one unit, for example if the patient is one year older on diagnosis. For every additional year of patient age on diagnosis, the risk of death falls by 7% (hazard ratio 0.93). | |
C2869 | The INPUT function returns the value produced when a SAS expression is read using a specified informat. You must use an assignment statement to store that value in a variable. The INPUT statement uses an informat to read a data value and then optionally stores that value in a variable. Examples. | |
C2870 | “Deep learning is a branch of machine learning that uses neural networks with many layers. A deep neural network analyzes data with learned representations similarly to the way a person would look at a problem,” Brock says. “In traditional machine learning, the algorithm is given a set of relevant features to analyze. | |
C2871 | Decision tree builds regression or classification models in the form of a tree structure. It breaks down a dataset into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. The final result is a tree with decision nodes and leaf nodes. | |
C2872 | Generally, a machine learning pipeline describes or models your ML process: writing code, releasing it to production, performing data extractions, creating training models, and tuning the algorithm. An ML pipeline should be a continuous process as a team works on their ML platform. | |
C2873 | Sample variance Concretely, the naive estimator sums the squared deviations and divides by n, which is biased. The sample mean, on the other hand, is an unbiased estimator of the population mean μ. Note that the usual definition of sample variance is. , and this is an unbiased estimator of the population variance. | |
C2874 | Data analysis has two prominent methods: qualitative research and quantitative research. Each method has their own techniques. Interviews and observations are forms of qualitative research, while experiments and surveys are quantitative research. | |
C2875 | 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. | |
C2876 | In Computer science (especially Machine learning) Pruning means simplifying/compressing and optimizing a Decision tree by removing sections of the tree that are uncritical and redundant to classify instances. | |
C2877 | To work out the probability that a discrete random variable X takes a particular value x, we need to identify the event (the set of possible outcomes) that corresponds to "X=x". pX(x)=Pr(X=x). In general, the probability function pX(x) may be specified in a variety of ways. | |
C2878 | Data mining has several types, including pictorial data mining, text mining, social media mining, web mining, and audio and video mining amongst others.Read: Data Mining vs Machine Learning.Learn more: Association Rule Mining.Check out: Difference between Data Science and Data Mining.Read: Data Mining Project Ideas. | |
C2879 | The tobit model, also called a censored regression model, is designed to estimate linear relationships between variables when there is either left- or right-censoring in the dependent variable (also known as censoring from below and above, respectively). | |
C2880 | Y hat (written ŷ ) is the predicted value of y (the dependent variable) in a regression equation. It can also be considered to be the average value of the response variable. The equation is calculated during regression analysis. | |
C2881 | The optimal number of clusters can be defined as follow: Compute clustering algorithm (e.g., k-means clustering) for different values of k. For instance, by varying k from 1 to 10 clusters. For each k, calculate the total within-cluster sum of square (wss). Plot the curve of wss according to the number of clusters k. | |
C2882 | Output is defined as the act of producing something, the amount of something that is produced or the process in which something is delivered. An example of output is the electricity produced by a power plant. An example of output is producing 1,000 cases of a product. | |
C2883 | In artificial intelligence, eager learning is a learning method in which the system tries to construct a general, input-independent target function during training of the system, as opposed to lazy learning, where generalization beyond the training data is delayed until a query is made to the system. | |
C2884 | 1:006:34Suggested clip · 111 secondsPoisson regression interpreting SPSS results (brief demo) - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C2885 | for research. Instead, the bottleneck often is the lack of a solid research design and a credible theory, both of which are essential to develop, test, and accumulate causal explanations. This does not mean that big data has no benefits. | |
C2886 | Most implementations of random forest (and many other machine learning algorithms) that accept categorical inputs are either just automating the encoding of categorical features for you or using a method that becomes computationally intractable for large numbers of categories. A notable exception is H2O. | |
C2887 | Deep neural networks. A deep neural network (DNN) is an artificial neural network (ANN) with multiple layers between the input and output layers. For example, a DNN that is trained to recognize dog breeds will go over the given image and calculate the probability that the dog in the image is a certain breed. | |
C2888 | - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C2889 | In statistics and machine learning, the bias–variance tradeoff is the property of a model that the variance of the parameter estimates across samples can be reduced by increasing the bias in the estimated parameters. The bias error is an error from erroneous assumptions in the learning algorithm. | |
C2890 | The k-means clustering algorithm attempts to split a given anonymous data set (a set containing no information as to class identity) into a fixed number (k) of clusters. The resulting classifier is used to classify (using k = 1) the data and thereby produce an initial randomized set of clusters. | |
C2891 | Gladwell's purpose for writing The Outliers was to inform reader's on how successful people achieve success through the help of others, practice, and opportunity. He also wanted to get rid of our society's crude perspective on how outliers become successful. | |
C2892 | The Kolmogorov-Smirnov test (K-S) and Shapiro-Wilk (S-W) test are designed to test normality by comparing your data to a normal distribution with the same mean and standard deviation of your sample. If the test is NOT significant, then the data are normal, so any value above . 05 indicates normality. | |
C2893 | The size of the sample space is the total number of possible outcomes. For example, when you roll 1 die, the sample space is 1, 2, 3, 4, 5, or 6. So the size of the sample space is 6. | |
C2894 | Tf-idf stands for term frequency-inverse document frequency, and the tf-idf weight is a weight often used in information retrieval and text mining. This weight is a statistical measure used to evaluate how important a word is to a document in a collection or corpus. | |
C2895 | In probability theory and statistics, the binomial distribution with parameters n and p is the discrete probability distribution of the number of successes in a sequence of n independent experiments, each asking a yes–no question, and each with its own Boolean-valued outcome: success/yes/true/one (with probability p) | |
C2896 | Residual = Observed – Predicted positive values for the residual (on the y-axis) mean the prediction was too low, and negative values mean the prediction was too high; 0 means the guess was exactly correct. That is, (1) they're pretty symmetrically distributed, tending to cluster towards the middle of the plot. | |
C2897 | Use systematic sampling when there's low risk of data manipulation. Systematic sampling is the preferred method over simple random sampling when a study maintains a low risk of data manipulation. | |
C2898 | 0:031:06Suggested clip · 42 secondsLatent dirichlet allocation distortions - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C2899 | Convenience sampling (also known as grab sampling, accidental sampling, or opportunity sampling) is a type of non-probability sampling that involves the sample being drawn from that part of the population that is close to hand. |
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