From 84c4b706eadb52a9a13b98e5f603cee3cc4a7b10 Mon Sep 17 00:00:00 2001 From: Jasleen Sondhi Date: Fri, 15 Sep 2023 01:17:04 +0530 Subject: [PATCH 01/11] refactored logistic regression r lesson text --- .../4-Logistic/solution/R/lesson_4.Rmd | 33 ------------------- 1 file changed, 33 deletions(-) diff --git a/2-Regression/4-Logistic/solution/R/lesson_4.Rmd b/2-Regression/4-Logistic/solution/R/lesson_4.Rmd index 9184303e..c111bea3 100644 --- a/2-Regression/4-Logistic/solution/R/lesson_4.Rmd +++ b/2-Regression/4-Logistic/solution/R/lesson_4.Rmd @@ -83,9 +83,7 @@ There are other types of logistic regression, including multinomial and ordinal: ![Multinomial vs ordinal regression](https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/4-Logistic/images/multinomial-vs-ordinal.png) \ -**It's still linear** -Even though this type of Regression is all about 'category predictions', it still works best when there is a clear linear relationship between the dependent variable (color) and the other independent variables (the rest of the dataset, like city name and size). It's good to get an idea of whether there is any linearity dividing these variables or not. #### **Variables DO NOT have to correlate** @@ -232,35 +230,6 @@ Now that we have an idea of the relationship between the binary categories of co ## 3. Build your model -> **๐Ÿงฎ Show Me The Math** -> -> Remember how `linear regression` often used `ordinary least squares` to arrive at a value? `Logistic regression` relies on the concept of 'maximum likelihood' using [`sigmoid functions`](https://wikipedia.org/wiki/Sigmoid_function). A Sigmoid Function on a plot looks like an `S shape`. It takes a value and maps it to somewhere between 0 and 1. Its curve is also called a 'logistic curve'. Its formula looks like this: -> -> ![](../../images/sigmoid.png) -> -> where the sigmoid's midpoint finds itself at x's 0 point, L is the curve's maximum value, and k is the curve's steepness. If the outcome of the function is more than 0.5, the label in question will be given the class 1 of the binary choice. If not, it will be classified as 0. - -Let's begin by splitting the data into `training` and `test` sets. The training set is used to train a classifier so that it finds a statistical relationship between the features and the label value. - -It is best practice to hold out some of your data for **testing** in order to get a better estimate of how your models will perform on new data by comparing the predicted labels with the already known labels in the test set. [rsample](https://rsample.tidymodels.org/), a package in Tidymodels, provides infrastructure for efficient data splitting and resampling: - -```{r split_data} -# Split data into 80% for training and 20% for testing -set.seed(2056) -pumpkins_split <- pumpkins_select %>% - initial_split(prop = 0.8) - -# Extract the data in each split -pumpkins_train <- training(pumpkins_split) -pumpkins_test <- testing(pumpkins_split) - -# Print out the first 5 rows of the training set -pumpkins_train %>% - slice_head(n = 5) - - -``` - ๐Ÿ™Œ We are now ready to train a model by fitting the training features to the training label (color). We'll begin by creating a recipe that specifies the preprocessing steps that should be carried out on our data to get it ready for modelling i.e: encoding categorical variables into a set of integers. Just like `baked_pumpkins`, we create a `pumpkins_recipe` but do not `prep` and `bake` since it would be bundled into a workflow, which you will see in just a few steps from now. @@ -418,5 +387,3 @@ But for now, congratulations ๐ŸŽ‰๐ŸŽ‰๐ŸŽ‰! You've completed these regression les You R awesome! ![Artwork by \@allison_horst](../../images/r_learners_sm.jpeg) - - From ab24e44a520bf10056772c3a616077af557c691a Mon Sep 17 00:00:00 2001 From: Jasleen Sondhi Date: Fri, 15 Sep 2023 01:25:35 +0530 Subject: [PATCH 02/11] refactoring logistic regression r text --- .../4-Logistic/solution/R/lesson_4.Rmd | 21 +++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/2-Regression/4-Logistic/solution/R/lesson_4.Rmd b/2-Regression/4-Logistic/solution/R/lesson_4.Rmd index c111bea3..f87fd919 100644 --- a/2-Regression/4-Logistic/solution/R/lesson_4.Rmd +++ b/2-Regression/4-Logistic/solution/R/lesson_4.Rmd @@ -230,6 +230,27 @@ Now that we have an idea of the relationship between the binary categories of co ## 3. Build your model +Let's begin by splitting the data into `training` and `test` sets. The training set is used to train a classifier so that it finds a statistical relationship between the features and the label value. + +It is best practice to hold out some of your data for **testing** in order to get a better estimate of how your models will perform on new data by comparing the predicted labels with the already known labels in the test set. [rsample](https://rsample.tidymodels.org/), a package in Tidymodels, provides infrastructure for efficient data splitting and resampling: + +```{r split_data} +# Split data into 80% for training and 20% for testing +set.seed(2056) +pumpkins_split <- pumpkins_select %>% + initial_split(prop = 0.8) + +# Extract the data in each split +pumpkins_train <- training(pumpkins_split) +pumpkins_test <- testing(pumpkins_split) + +# Print out the first 5 rows of the training set +pumpkins_train %>% + slice_head(n = 5) + + +``` + ๐Ÿ™Œ We are now ready to train a model by fitting the training features to the training label (color). We'll begin by creating a recipe that specifies the preprocessing steps that should be carried out on our data to get it ready for modelling i.e: encoding categorical variables into a set of integers. Just like `baked_pumpkins`, we create a `pumpkins_recipe` but do not `prep` and `bake` since it would be bundled into a workflow, which you will see in just a few steps from now. From 6fddfa55c6a8d814c735e0aa18313ed78e1e19c1 Mon Sep 17 00:00:00 2001 From: Jasleen Sondhi Date: Fri, 15 Sep 2023 02:12:19 +0530 Subject: [PATCH 03/11] added categorical plot for pumpkin colors variety --- 2-Regression/4-Logistic/solution/R/lesson_4.Rmd | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/2-Regression/4-Logistic/solution/R/lesson_4.Rmd b/2-Regression/4-Logistic/solution/R/lesson_4.Rmd index c111bea3..09aca861 100644 --- a/2-Regression/4-Logistic/solution/R/lesson_4.Rmd +++ b/2-Regression/4-Logistic/solution/R/lesson_4.Rmd @@ -226,6 +226,19 @@ baked_pumpkins %>% theme(legend.position = "none") ``` + +```{r cat plot pumpkins-colors-variety} +# Specify colors for each value of the hue variable +palette <- c(ORANGE = "orange", WHITE = "wheat") + +# Create the bar plot +ggplot(pumpkins, aes(y = Variety, fill = Color)) + + geom_bar(position = "dodge") + + scale_fill_manual(values = palette) + + labs(y = "Variety", fill = "Color") + + theme_minimal() +``` + Now that we have an idea of the relationship between the binary categories of color and the larger group of sizes, let's explore logistic regression to determine a given pumpkin's likely color. ## 3. Build your model From 435f1ed598f8617ceff35357122f1780c4d7f4ec Mon Sep 17 00:00:00 2001 From: Jasleen Sondhi Date: Sat, 16 Sep 2023 13:58:43 +0530 Subject: [PATCH 04/11] removed box plot and added cat plot --- .../4-Logistic/solution/R/lesson_4.Rmd | 35 +++++++------------ 1 file changed, 13 insertions(+), 22 deletions(-) diff --git a/2-Regression/4-Logistic/solution/R/lesson_4.Rmd b/2-Regression/4-Logistic/solution/R/lesson_4.Rmd index aee56f4e..81963593 100644 --- a/2-Regression/4-Logistic/solution/R/lesson_4.Rmd +++ b/2-Regression/4-Logistic/solution/R/lesson_4.Rmd @@ -192,18 +192,18 @@ baked_pumpkins_long %>% ``` -Now, let's make some boxplots showing the distribution of the predictors with respect to the outcome color! +Now, let's make a categorical plot showing the distribution of the predictors with respect to the outcome color! -```{r boxplots} -theme_set(theme_light()) -#Make a box plot for each predictor feature -baked_pumpkins_long %>% - mutate(color = factor(color)) %>% - ggplot(mapping = aes(x = color, y = values, fill = features)) + - geom_boxplot() + - facet_wrap(~ features, scales = "free", ncol = 3) + - scale_color_viridis_d(option = "cividis", end = .8) + - theme(legend.position = "none") +```{r cat plot pumpkins-colors-variety} +# Specify colors for each value of the hue variable +palette <- c(ORANGE = "orange", WHITE = "wheat") + +# Create the bar plot +ggplot(pumpkins, aes(y = Variety, fill = Color)) + + geom_bar(position = "dodge") + + scale_fill_manual(values = palette) + + labs(y = "Variety", fill = "Color") + + theme_minimal() ``` Amazing๐Ÿคฉ! For some of the features, there's a noticeable difference in the distribution for each color label. For instance, it seems the white pumpkins can be found in smaller packages and in some particular varieties of pumpkins. The *item_size* category also seems to make a difference in the color distribution. These features may help predict the color of a pumpkin. @@ -227,19 +227,10 @@ baked_pumpkins %>% ``` -```{r cat plot pumpkins-colors-variety} -# Specify colors for each value of the hue variable -palette <- c(ORANGE = "orange", WHITE = "wheat") +Now that we have an idea of the relationship between the binary categories of color and the larger group of sizes, let's explore logistic regression to determine a given pumpkin's likely color. -# Create the bar plot -ggplot(pumpkins, aes(y = Variety, fill = Color)) + - geom_bar(position = "dodge") + - scale_fill_manual(values = palette) + - labs(y = "Variety", fill = "Color") + - theme_minimal() -``` -Now that we have an idea of the relationship between the binary categories of color and the larger group of sizes, let's explore logistic regression to determine a given pumpkin's likely color. +### **Analysing relationships between features and label** ## 3. Build your model From 8b7d50a9ecd8acba747d1d15325ef73cb3c0849f Mon Sep 17 00:00:00 2001 From: Jasleen Sondhi Date: Sun, 17 Sep 2023 02:42:36 +0530 Subject: [PATCH 05/11] added plot for relationship bw features and labels --- .../4-Logistic/solution/R/lesson_4.Rmd | 47 ++++++++++++------- 1 file changed, 30 insertions(+), 17 deletions(-) diff --git a/2-Regression/4-Logistic/solution/R/lesson_4.Rmd b/2-Regression/4-Logistic/solution/R/lesson_4.Rmd index 81963593..444d3852 100644 --- a/2-Regression/4-Logistic/solution/R/lesson_4.Rmd +++ b/2-Regression/4-Logistic/solution/R/lesson_4.Rmd @@ -177,21 +177,6 @@ baked_pumpkins %>% slice_head(n = 5) ``` -Now let's compare the feature distributions for each label value using box plots. We'll begin by formatting the data to a *long* format to make it somewhat easier to make multiple `facets`. - -```{r pivot} -# Pivot data to long format -baked_pumpkins_long <- baked_pumpkins %>% - pivot_longer(!color, names_to = "features", values_to = "values") - - -# Print out restructured data -baked_pumpkins_long %>% - slice_head(n = 10) - -``` - - Now, let's make a categorical plot showing the distribution of the predictors with respect to the outcome color! ```{r cat plot pumpkins-colors-variety} @@ -208,6 +193,36 @@ ggplot(pumpkins, aes(y = Variety, fill = Color)) + Amazing๐Ÿคฉ! For some of the features, there's a noticeable difference in the distribution for each color label. For instance, it seems the white pumpkins can be found in smaller packages and in some particular varieties of pumpkins. The *item_size* category also seems to make a difference in the color distribution. These features may help predict the color of a pumpkin. +### **Analysing relationships between features and label** + +```{r} + +# Define the color palette +palette <- c(ORANGE = "orange", WHITE = "wheat") + +# We need the encoded Item Size column to use it as the x-axis values in the plot +pumpkins_select$item_size <- baked_pumpkins$item_size + +# Create the grouped box plot +ggplot(pumpkins_select, aes(x = `item_size`, y = color, fill = color)) + + geom_boxplot() + + facet_grid(variety ~ ., scales = "free_x") + + scale_fill_manual(values = palette) + + labs(x = "Item Size", y = "") + + theme_minimal() + + theme(strip.text = element_text(size = 12)) + + theme(axis.text.x = element_text(size = 10)) + + theme(axis.title.x = element_text(size = 12)) + + theme(axis.title.y = element_blank()) + + theme(legend.position = "bottom") + + guides(fill = guide_legend(title = "Color")) + + theme(panel.spacing = unit(2.0, "lines"))+ + theme(strip.text.y = element_text(size = 4, hjust = 0)) + +``` + +Let's now focus on a specific relationship: Item Size and Color! + #### **Use a swarm plot** Color is a binary category (Orange or Not), it's called `categorical data`. There are other various ways of [visualizing categorical data](https://seaborn.pydata.org/tutorial/categorical.html?highlight=bar). @@ -230,8 +245,6 @@ baked_pumpkins %>% Now that we have an idea of the relationship between the binary categories of color and the larger group of sizes, let's explore logistic regression to determine a given pumpkin's likely color. -### **Analysing relationships between features and label** - ## 3. Build your model Let's begin by splitting the data into `training` and `test` sets. The training set is used to train a classifier so that it finds a statistical relationship between the features and the label value. From f0f20653d00fd91a90ba1649573b0b171e45aae6 Mon Sep 17 00:00:00 2001 From: Jasleen Sondhi Date: Sun, 17 Sep 2023 02:51:40 +0530 Subject: [PATCH 06/11] fixed spacing and variables in plots --- 2-Regression/4-Logistic/solution/R/lesson_4.Rmd | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/2-Regression/4-Logistic/solution/R/lesson_4.Rmd b/2-Regression/4-Logistic/solution/R/lesson_4.Rmd index 444d3852..7f5357e6 100644 --- a/2-Regression/4-Logistic/solution/R/lesson_4.Rmd +++ b/2-Regression/4-Logistic/solution/R/lesson_4.Rmd @@ -184,7 +184,7 @@ Now, let's make a categorical plot showing the distribution of the predictors wi palette <- c(ORANGE = "orange", WHITE = "wheat") # Create the bar plot -ggplot(pumpkins, aes(y = Variety, fill = Color)) + +ggplot(pumpkins_select, aes(y = variety, fill = color)) + geom_bar(position = "dodge") + scale_fill_manual(values = palette) + labs(y = "Variety", fill = "Color") + @@ -216,7 +216,7 @@ ggplot(pumpkins_select, aes(x = `item_size`, y = color, fill = color)) + theme(axis.title.y = element_blank()) + theme(legend.position = "bottom") + guides(fill = guide_legend(title = "Color")) + - theme(panel.spacing = unit(2.0, "lines"))+ + theme(panel.spacing = unit(0.5, "lines"))+ theme(strip.text.y = element_text(size = 4, hjust = 0)) ``` From 81db2c85f443760b198eb8023a60d01c2dfe48b3 Mon Sep 17 00:00:00 2001 From: Jasleen Sondhi Date: Sun, 17 Sep 2023 03:25:53 +0530 Subject: [PATCH 07/11] fixed spacing --- 2-Regression/4-Logistic/solution/R/lesson_4.Rmd | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/2-Regression/4-Logistic/solution/R/lesson_4.Rmd b/2-Regression/4-Logistic/solution/R/lesson_4.Rmd index 7f5357e6..18407b31 100644 --- a/2-Regression/4-Logistic/solution/R/lesson_4.Rmd +++ b/2-Regression/4-Logistic/solution/R/lesson_4.Rmd @@ -82,8 +82,6 @@ There are other types of logistic regression, including multinomial and ordinal: ![Multinomial vs ordinal regression](https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/4-Logistic/images/multinomial-vs-ordinal.png) -\ - #### **Variables DO NOT have to correlate** @@ -306,6 +304,7 @@ log_reg_wf After a workflow has been *specified*, a model can be `trained` using the [`fit()`](https://tidymodels.github.io/parsnip/reference/fit.html) function. The workflow will estimate a recipe and preprocess the data before training, so we won't have to manually do that using prep and bake. + ```{r train} # Train the model wf_fit <- log_reg_wf %>% @@ -345,8 +344,6 @@ The [**`conf_mat()`**](https://tidymodels.github.io/yardstick/reference/conf_mat ```{r conf_mat} # Confusion matrix for prediction results conf_mat(data = results, truth = color, estimate = .pred_class) - - ``` Let's interpret the confusion matrix. Our model is asked to classify pumpkins between two binary categories, category `white` and category `not-white` From 1b6274f711abec765549d1c949da065044e14879 Mon Sep 17 00:00:00 2001 From: Jasleen Sondhi Date: Sun, 17 Sep 2023 03:30:02 +0530 Subject: [PATCH 08/11] refactored text --- 2-Regression/4-Logistic/solution/R/lesson_4.Rmd | 17 ----------------- 1 file changed, 17 deletions(-) diff --git a/2-Regression/4-Logistic/solution/R/lesson_4.Rmd b/2-Regression/4-Logistic/solution/R/lesson_4.Rmd index 18407b31..16610df3 100644 --- a/2-Regression/4-Logistic/solution/R/lesson_4.Rmd +++ b/2-Regression/4-Logistic/solution/R/lesson_4.Rmd @@ -72,17 +72,6 @@ Logistic regression does not offer the same features as linear regression. The f ![Infographic by Dasani Madipalli](../../images/pumpkin-classifier.png){width="600"} -#### **Other classifications** - -There are other types of logistic regression, including multinomial and ordinal: - -- **Multinomial**, which involves having more than one category - "Orange, White, and Striped". - -- **Ordinal**, which involves ordered categories, useful if we wanted to order our outcomes logically, like our pumpkins that are ordered by a finite number of sizes (mini,sm,med,lg,xl,xxl). - -![Multinomial vs ordinal regression](https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/4-Logistic/images/multinomial-vs-ordinal.png) - - #### **Variables DO NOT have to correlate** Remember how linear regression worked better with more correlated variables? Logistic regression is the opposite - the variables don't have to align. That works for this data which has somewhat weak correlations. @@ -238,11 +227,8 @@ baked_pumpkins %>% scale_color_brewer(palette = "Dark2", direction = -1) + theme(legend.position = "none") ``` - - Now that we have an idea of the relationship between the binary categories of color and the larger group of sizes, let's explore logistic regression to determine a given pumpkin's likely color. - ## 3. Build your model Let's begin by splitting the data into `training` and `test` sets. The training set is used to train a classifier so that it finds a statistical relationship between the features and the label value. @@ -262,8 +248,6 @@ pumpkins_test <- testing(pumpkins_split) # Print out the first 5 rows of the training set pumpkins_train %>% slice_head(n = 5) - - ``` ๐Ÿ™Œ We are now ready to train a model by fitting the training features to the training label (color). @@ -299,7 +283,6 @@ log_reg_wf <- workflow() %>% # Print out the workflow log_reg_wf - ``` After a workflow has been *specified*, a model can be `trained` using the [`fit()`](https://tidymodels.github.io/parsnip/reference/fit.html) function. The workflow will estimate a recipe and preprocess the data before training, so we won't have to manually do that using prep and bake. From 066bf89132c910f9a2c70c9b669818528acd92a8 Mon Sep 17 00:00:00 2001 From: Jasleen Sondhi Date: Tue, 19 Sep 2023 21:21:14 +0530 Subject: [PATCH 09/11] fixed plot and modelling error --- 2-Regression/4-Logistic/solution/R/lesson_4.Rmd | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/2-Regression/4-Logistic/solution/R/lesson_4.Rmd b/2-Regression/4-Logistic/solution/R/lesson_4.Rmd index 16610df3..971fc56b 100644 --- a/2-Regression/4-Logistic/solution/R/lesson_4.Rmd +++ b/2-Regression/4-Logistic/solution/R/lesson_4.Rmd @@ -188,10 +188,11 @@ Amazing๐Ÿคฉ! For some of the features, there's a noticeable difference in the di palette <- c(ORANGE = "orange", WHITE = "wheat") # We need the encoded Item Size column to use it as the x-axis values in the plot -pumpkins_select$item_size <- baked_pumpkins$item_size +pumpkins_select_plot<-pumpkins_select +pumpkins_select_plot$item_size <- baked_pumpkins$item_size # Create the grouped box plot -ggplot(pumpkins_select, aes(x = `item_size`, y = color, fill = color)) + +ggplot(pumpkins_select_plot, aes(x = `item_size`, y = color, fill = color)) + geom_boxplot() + facet_grid(variety ~ ., scales = "free_x") + scale_fill_manual(values = palette) + @@ -296,6 +297,7 @@ wf_fit <- log_reg_wf %>% # Print the trained workflow wf_fit + ``` The model print out shows the coefficients learned during training. 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