Решение 1 3: Решение задачи 9 – 3 ÷ 1/3 + 1 =?

Решение уравнений с параметром онлайн · Как пользоваться Контрольная Работа РУ

Сайт решает несколько типов уравнений с параметрами:

• линейные с параметром
• квадратные с параметром

Например, если требуется решить линейное уравнение с параметром: (a^2-1)*x = 1 + a

Дано уравнение с параметром: $$x \left(a^{2} — 1\right) = a + 1$$ Коэффициент при x равен $$a^{2} — 1$$ тогда возможные случаи для a : $$a < -1$$ $$a = -1$$ $$a > -1 \wedge a < 1$$ $$a = 1$$ Рассмотри все случаи подробнее:
При $$a < -1$$ уравнение будет $$3 x + 1 = 0$$ его решение $$x = — \frac{1}{3}$$ При $$a = -1$$ уравнение будет $$0 = 0$$ его решение — любое x При $$a > -1 \wedge a < 1$$ уравнение будет $$- x — 1 = 0$$ его решение $$x = -1$$ При $$a = 1$$ уравнение будет $$-2 = 0$$ его решение: нет решений

Пример решения квадратного уравнения с параметром

(a^2-1)*x^2 = (8 + 9*a)*x + 1

Дано уравнение с параметром: $$x^{2} \left(a^{2} — 1\right) = x \left(9 a + 8\right) + 1$$ Коэффициент при x равен $$a^{2} — 1$$ тогда возможные случаи для a : $$a < -1$$ $$a = -1$$ $$a > -1 \wedge a < 1$$ $$a = 1$$ Рассмотри все случаи подробнее:
При $$a < -1$$ уравнение будет $$3 x^{2} + 10 x — 1 = 0$$ его решение $$x = — \frac{5}{3} + \frac{2 \sqrt{7}}{3}$$ $$x = — \frac{2 \sqrt{7}}{3} — \frac{5}{3}$$ При $$a = -1$$ уравнение будет $$x — 1 = 0$$ его решение $$x = 1$$ При $$a > -1 \wedge a < 1$$ уравнение будет $$- x^{2} — 8 x — 1 = 0$$ его решение $$x = -4 — \sqrt{15}$$ $$x = -4 + \sqrt{15}$$ При $$a = 1$$ уравнение будет $$- 17 x — 1 = 0$$ его решение $$x = — \frac{1}{17}$$

90000 1.10. Decision Trees — scikit-learn 0.23.1 documentation 90001 90002 90003 Decision Trees (DTs) 90004 are a non-parametric supervised learning method used for classification and regression. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features. 90005 90002 For instance, in the example below, decision trees learn from data to approximate a sine curve with a set of if-then-else decision rules.The deeper the tree, the more complex the decision rules and the fitter the model. 90005 90002 Some advantages of decision trees are: 90005 90010 90011 90012 90002 Simple to understand and to interpret. Trees can be visualised. 90005 90015 90012 90002 Requires little data preparation. Other techniques often require data normalisation, dummy variables need to be created and blank values ​​to be removed. Note however that this module does not support missing values. 90005 90015 90012 90002 The cost of using the tree (i.e., predicting data) is logarithmic in the number of data points used to train the tree. 90005 90015 90012 90002 Able to handle both numerical and categorical data. Other techniques are usually specialised in analysing datasets that have only one type of variable. See algorithms for more information. 90005 90015 90012 90002 Able to handle multi-output problems. 90005 90015 90012 90002 Uses a white box model. If a given situation is observable in a model, the explanation for the condition is easily explained by boolean logic.By contrast, in a black box model (e.g., in an artificial neural network), results may be more difficult to interpret. 90005 90015 90012 90002 Possible to validate a model using statistical tests. That makes it possible to account for the reliability of the model. 90005 90015 90012 90002 Performs well even if its assumptions are somewhat violated by the true model from which the data were generated. 90005 90015 90044 90045 90002 The disadvantages of decision trees include: 90005 90010 90011 90012 90002 Decision-tree learners can create over-complex trees that do not generalise the data well.This is called overfitting. Mechanisms such as pruning, setting the minimum number of samples required at a leaf node or setting the maximum depth of the tree are necessary to avoid this problem. 90005 90015 90012 90002 Decision trees can be unstable because small variations in the data might result in a completely different tree being generated. This problem is mitigated by using decision trees within an ensemble. 90005 90015 90012 90002 The problem of learning an optimal decision tree is known to be NP-complete under several aspects of optimality and even for simple concepts.Consequently, practical decision-tree learning algorithms are based on heuristic algorithms such as the greedy algorithm where locally optimal decisions are made at each node. Such algorithms can not guarantee to return the globally optimal decision tree. This can be mitigated by training multiple trees in an ensemble learner, where the features and samples are randomly sampled with replacement. 90005 90015 90012 90002 There are concepts that are hard to learn because decision trees do not express them easily, such as XOR, parity or multiplexer problems.90005 90015 90012 90002 Decision tree learners create biased trees if some classes dominate. It is therefore recommended to balance the dataset prior to fitting with the decision tree. 90005 90015 90044 90045 90072 1.10.1. Classification 90073 90002 90075 DecisionTreeClassifier 90076 is a class capable of performing multi-class classification on a dataset. 90005 90002 As with other classifiers, 90075 DecisionTreeClassifier 90076 takes as input two arrays: an array X, sparse or dense, of size 90075 [n_samples, n_features] 90076 holding the training samples, and an array Y of integer values, size 90075 [n_samples] 90076, holding the class labels for the training samples: 90005 90086 > >> from sklearn import tree >>> X = [[0, 0], [1, 1]] >>> Y = [0, 1] >>> clf = tree.DecisionTreeClassifier () >>> clf = clf.fit (X, Y) 90087 90002 After being fitted, the model can then be used to predict the class of samples: 90005 90086 >>> clf.predict ([[2., 2.]]) array ([1]) 90087 90002 Alternatively, the probability of each class can be predicted, which is the fraction of training samples of the same class in a leaf: 90005 90086 >>> clf.predict_proba ([[2., 2.]]) array ([[0., 1.]]) 90087 90002 90075 DecisionTreeClassifier 90076 is capable of both binary (where the labels are [-1, 1]) classification and multiclass (where the labels are [0, …, K-1]) classification.90005 90002 Using the Iris dataset, we can construct a tree as follows: 90005 90086 > >> from sklearn.datasets import load_iris >>> from sklearn import tree >>> X, y = load_iris (return_X_y = True) >>> clf = tree.DecisionTreeClassifier () >>> clf = clf.fit (X, y) 90087 90002 Once trained, you can plot the tree with the 90075 plot_tree 90076 function: 90005 .90000 Page not found · GitHub Pages 90001 Page not found · GitHub Pages 90002 90003 File not found 90004 90005 90002 The site configured at this address does not contain the requested file. 90005 90002 If this is your site, make sure that the filename case matches the URL. 90009 For root URLs (like 90010 http://example.com/ 90011) you must provide an 90010 index.html 90011 file. 90005 90002 Read the full documentation for more information about using 90003 GitHub Pages 90004.90005 .