Memprediksi dengan decision tree

decision tree

Berikut adalah decision tree yang dipelajari dari himpunan data otak yang ditampilkan pada video sebelumnya. Pohon ini memprediksi tingkat kecerdasan yang diharapkan (manusia memiliki kecerdasan 1) dari beberapa mamalia, sebagai fungsi dari gestation time (dalam hari) dan rata-rata litter size untuk spesies tersebut.

Node daun menampilkan ukuran otak yang diharapkan untuk setiap datum dalam node tersebut, serta berapa banyak (dalam persentase) datum yang masuk ke node itu.

Anda ingin memprediksi kecerdasan gorila, yang memiliki gestation time 265 hari dan rata-rata litter size 1.

Ukuran otak relatif berapa yang diprediksi oleh model pohon ini?

Latihan ini adalah bagian dari kursus

Supervised Learning di R: Regresi

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Mulai berolahraga

Latihan ini adalah bagian dari kursus

Supervised Learning di R: Regresi

SkillTag.level.intermediateSkillTag.label

4.6+

Mulai Kursus Gratis

In this chapter we introduce the concept of regression from a machine learning point of view. We will present the fundamental regression method: linear regression. We will show how to fit a linear regression model and to make predictions from the model.

Exercise 1: Welcome and Introduction Exercise 2: Identify the regression tasks Exercise 3: Linear regression - the fundamental method Exercise 4: Code a simple one-variable regression Exercise 5: Examining a model Exercise 6: Predicting once you fit a model Exercise 7: Predicting from the unemployment model Exercise 8: Multivariate linear regression (Part 1)Exercise 9: Multivariate linear regression (Part 2)Exercise 10: Wrapping up linear regression

Now that we have learned how to fit basic linear regression models, we will learn how to evaluate how well our models perform. We will review evaluating a model graphically, and look at two basic metrics for regression models. We will also learn how to train a model that will perform well in the wild, not just on training data. Although we will demonstrate these techniques using linear regression, all these concepts apply to models fit with any regression algorithm.

Exercise 1: Evaluating a model graphically Exercise 2: Graphically evaluate the unemployment model Exercise 3: The gain curve to evaluate the unemployment model Exercise 4: Root Mean Squared Error (RMSE)Exercise 5: Calculate RMSE Exercise 6: R-squared Exercise 7: Calculate R-squared Exercise 8: Correlation and R-squared Exercise 9: Properly Training a Model Exercise 10: Generating a random test/train split Exercise 11: Train a model using test/train split Exercise 12: Evaluate a model using test/train split Exercise 13: Create a cross validation plan Exercise 14: Evaluate a modeling procedure using n-fold cross-validation

Before moving on to more sophisticated regression techniques, we will look at some other modeling issues: modeling with categorical inputs, interactions between variables, and when you might consider transforming inputs and outputs before modeling. While more sophisticated regression techniques manage some of these issues automatically, it's important to be aware of them, in order to understand which methods best handle various issues -- and which issues you must still manage yourself.

Exercise 1: Categorical inputs Exercise 2: Examining the structure of categorical inputs Exercise 3: Modeling with categorical inputs Exercise 4: Interactions Exercise 5: Modeling an interaction Exercise 6: Modeling an interaction (2)Exercise 7: Transforming the response before modeling Exercise 8: Relative error Exercise 9: Modeling log-transformed monetary output Exercise 10: Comparing RMSE and root-mean-squared Relative Error Exercise 11: Transforming inputs before modeling Exercise 12: Input transforms: the "hockey stick"Exercise 13: Input transforms: the "hockey stick" (2)

Now that we have mastered linear models, we will begin to look at techniques for modeling situations that don't meet the assumptions of linearity. This includes predicting probabilities and frequencies (values bounded between 0 and 1); predicting counts (nonnegative integer values, and associated rates); and responses that have a non-linear but additive relationship to the inputs. These algorithms are variations on the standard linear model.

Exercise 1: Logistic regression to predict probabilities Exercise 2: Fit a model of sparrow survival probability Exercise 3: Predict sparrow survival Exercise 4: Poisson and quasipoisson regression to predict counts Exercise 5: Poisson or quasipoisson Exercise 6: Fit a model to predict bike rental counts Exercise 7: Predict bike rentals on new data Exercise 8: Visualize the bike rental predictions Exercise 9: GAM to learn non-linear transforms Exercise 10: Writing formulas for GAM models Exercise 11: Writing formulas for GAM models (2)Exercise 12: Model soybean growth with GAM Exercise 13: Predict with the soybean model on test data

In this chapter we will look at modeling algorithms that do not assume linearity or additivity, and that can learn limited types of interactions among input variables. These algorithms are *tree-based* methods that work by combining ensembles of *decision trees* that are learned from the training data.

Exercise 1: Intuisi di balik metode berbasis pohon Exercise 2: Memprediksi dengan decision tree

Latihan Saat Ini

Exercise 3: Random forests Exercise 4: Bangun model random forest untuk penyewaan sepeda Exercise 5: Prediksi penyewaan sepeda dengan model random forest Exercise 6: Visualisasikan prediksi model random forest untuk sepeda Exercise 7: One-Hot-Encoding Variabel Kategorikal Exercise 8: vtreat pada contoh kecil Exercise 9: Level baru (novel levels)Exercise 10: vtreat data penyewaan sepeda Exercise 11: Gradient boosting machines Exercise 12: Temukan jumlah pohon yang tepat untuk gradient boosting machine Exercise 13: Melatih model xgboost untuk penyewaan sepeda dan melakukan prediksi Exercise 14: Evaluasi model penyewaan sepeda xgboost Exercise 15: Visualisasikan model penyewaan sepeda xgboost